{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.APPENDIX E.04-21-2023.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}22 Apr 2023, 09:54:36
{txt}
{com}. 
. 
. 
. *******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
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. 
. 
. 
. 
. **** APPENDIX E STATISTICAL ANALYSES: REPLICATE MANUSCRIPT MODELS -- INCLUSION OF POST-EMPLOYMENT UNIT EFFECTS TO CONTROL FOR SYSTEMATIC DIFFERENCES ATTRIBUTABLE TO DEPARTURE REASONS ***
. 
. 
. 
. ** RETRIEVE SINGLE EVENT RECORDS DATABASE [N = 860 APPOINTEE OBSERVATIONS: 831 UNCENSORED OBSERVATIONS; 29 CENSORED OBSERVATIONS] **
. 
. use "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Data\Krause and Byers.SRD.06-03-2022.dta", replace
{txt}
{com}. 
. 
. 
. 
. *** DESCRIPTIVE STATISTICS OF POST-EMPLOYMENT VARIABLE [REASONS FOR DEPARTURE: NOTE: "9999" REPRESENTS THOSE CASES WHERE A REASON FOR DEPARTURE COULD NOT BE LOCATED]
. *** SEE CODEBOOK FOR VARIABLE OPERATIONALIZATION AND DEFINITIONS [NOTE: THIS MEASURE IS EMPLOYED TO ESTIMATE THE COMPETING RISKS MODELS IN APPENDIX A] ***
. 
. 
. tab postemployment

{txt}postemploym {c |}
        ent {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         22        2.56        2.56
{txt}          2 {c |}{res}          4        0.47        3.02
{txt}          3 {c |}{res}         74        8.60       11.63
{txt}          4 {c |}{res}         21        2.44       14.07
{txt}          5 {c |}{res}        180       20.93       35.00
{txt}          6 {c |}{res}        476       55.35       90.35
{txt}          7 {c |}{res}         50        5.81       96.16
{txt}       9999 {c |}{res}         33        3.84      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        860      100.00
{txt}
{com}. 
. 
. 
. 
. 
. *******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. ** GENERATE CENSORING VARIABLE FOR HOLDOVER APPOINTEES SERVING BETWEEN/ACROSS ADMINISTRATIONS [=1]; UNCENSRED OBSERVATIONS [=0] ** 
. 
. gen singleadmin_service=1 if holdover==0
{txt}(29 missing values generated)

{com}. *
. replace singleadmin_service=0 if holdover==1
{txt}(29 real changes made)

{com}. *
. *
. tab singleadmin_service

{txt}singleadmin {c |}
   _service {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         29        3.37        3.37
{txt}          1 {c |}{res}        831       96.63      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        860      100.00
{txt}
{com}. 
. 
. ** SET FOR SURVIVAL DATA WITH A SINGLE RECORD PER APPOINTEE OBSERVATION [N = 860: UNCENSORED N = 831; CENSORED N = 29] ** 
. stset okapptdur, failure(singleadmin_service)

     {txt}failure event:  {res}singleadmin_service != 0 & singleadmin_service < .
{txt}obs. time interval:  {res}(0, okapptdur]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}        860{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}        860{txt}  observations remaining, representing
{res}        831{txt}  failures in single-record/single-failure data
{res}    850,034{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}    4,074
{txt}
{com}. *
. 
. *
. *
. *
. *
. 
. ** ESTIMATE COX SEMIPARAMETRIC AND WEIBULL PARAMETRIC MODELS PRESENTED IN MANUSCRIPT [MODELS F1 - F4] ** 
. 
. ** NOTE COVARIATES THAT VARY TRHOUGH TIME ARE BASED ON THE STARTING DATE OF APPOINTED SERVICE [I.E., "OKSTART....""]
. 
. 
. 
. 
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *** MANUSCRIPT-BASED SURVIVAL REGRESSION ANALYSES: COX SEMIPARAMETRIC & WEIBULL PARAMETRIC MODELS ****
. 
. 
. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** APPENDIX E REGRESSION MODELS  ***
. 
. 
. 
. **** MODEL E1: COX MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr i.postemployment,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Iteration 0:   log pseudolikelihood = {res}-4793.4442
{txt}Iteration 1:   log pseudolikelihood = {res}-4524.9433
{txt}Iteration 2:   log pseudolikelihood = {res}-4506.0604
{txt}Iteration 3:   log pseudolikelihood = {res}-4505.8163
{txt}Iteration 4:   log pseudolikelihood = {res}-4505.8161
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-4505.8161

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}29{txt})    =  {res}   3104.49
{txt}Log pseudolikelihood =   {res}-4505.8161             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.358297{col 48}{space 2} .1433569{col 59}{space 1}    2.90{col 68}{space 3}0.004{col 76}{space 4} 1.104479{col 89}{space 3} 1.670444
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.079522{col 48}{space 2} .1110759{col 59}{space 1}    0.74{col 68}{space 3}0.457{col 76}{space 4} .8823653{col 89}{space 3} 1.320732
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6502516{col 48}{space 2} .0770747{col 59}{space 1}   -3.63{col 68}{space 3}0.000{col 76}{space 4} .5154517{col 89}{space 3}  .820304
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} .9830505{col 48}{space 2} .0668669{col 59}{space 1}   -0.25{col 68}{space 3}0.802{col 76}{space 4} .8603541{col 89}{space 3} 1.123245
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} 1.005637{col 48}{space 2} .0588743{col 59}{space 1}    0.10{col 68}{space 3}0.924{col 76}{space 4} .8966193{col 89}{space 3} 1.127909
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .6054495{col 48}{space 2} .0516967{col 59}{space 1}   -5.88{col 68}{space 3}0.000{col 76}{space 4} .5121505{col 89}{space 3} .7157449
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2}  1.49314{col 48}{space 2}  .135129{col 59}{space 1}    4.43{col 68}{space 3}0.000{col 76}{space 4} 1.250452{col 89}{space 3}  1.78293
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} 1.396743{col 48}{space 2} .1389136{col 59}{space 1}    3.36{col 68}{space 3}0.001{col 76}{space 4}  1.14937{col 89}{space 3} 1.697356
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.052484{col 48}{space 2} .1593198{col 59}{space 1}    0.34{col 68}{space 3}0.735{col 76}{space 4} .7822842{col 89}{space 3} 1.416009
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} .8352756{col 48}{space 2} .0721222{col 59}{space 1}   -2.08{col 68}{space 3}0.037{col 76}{space 4} .7052328{col 89}{space 3} .9892979
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} .0009714{col 48}{space 2} .0026007{col 59}{space 1}   -2.59{col 68}{space 3}0.010{col 76}{space 4} 5.11e-06{col 89}{space 3} .1846471
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 1.540657{col 48}{space 2}  .348615{col 59}{space 1}    1.91{col 68}{space 3}0.056{col 76}{space 4} .9887784{col 89}{space 3} 2.400564
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .1841127{col 48}{space 2} .0299921{col 59}{space 1}  -10.39{col 68}{space 3}0.000{col 76}{space 4} .1337896{col 89}{space 3} .2533642
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9950386{col 48}{space 2} .0034051{col 59}{space 1}   -1.45{col 68}{space 3}0.146{col 76}{space 4}  .988387{col 89}{space 3} 1.001735
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} .9312701{col 48}{space 2}  .044447{col 59}{space 1}   -1.49{col 68}{space 3}0.136{col 76}{space 4}  .848106{col 89}{space 3} 1.022589
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 1.931855{col 48}{space 2} .3436757{col 59}{space 1}    3.70{col 68}{space 3}0.000{col 76}{space 4} 1.363158{col 89}{space 3} 2.737806
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 5.537169{col 48}{space 2} .7289236{col 59}{space 1}   13.00{col 68}{space 3}0.000{col 76}{space 4} 4.277933{col 89}{space 3} 7.167069
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 4.633948{col 48}{space 2} 1.422206{col 59}{space 1}    5.00{col 68}{space 3}0.000{col 76}{space 4} 2.539269{col 89}{space 3} 8.456557
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.354205{col 48}{space 2} .1722741{col 59}{space 1}    2.38{col 68}{space 3}0.017{col 76}{space 4} 1.055357{col 89}{space 3} 1.737678
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.886379{col 48}{space 2} .4110793{col 59}{space 1}    7.44{col 68}{space 3}0.000{col 76}{space 4} 2.183358{col 89}{space 3} 3.815767
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 5.425279{col 48}{space 2} 1.393229{col 59}{space 1}    6.59{col 68}{space 3}0.000{col 76}{space 4} 3.279674{col 89}{space 3} 8.974564
{txt}{space 32}8  {c |}{col 36}{res}{space 2}  8.12461{col 48}{space 2} 2.393144{col 59}{space 1}    7.11{col 68}{space 3}0.000{col 76}{space 4} 4.561177{col 89}{space 3} 14.47199
{txt}{space 34} {c |}
{space 20}postemployment {c |}
{space 32}2  {c |}{col 36}{res}{space 2} .8989209{col 48}{space 2} .4824805{col 59}{space 1}   -0.20{col 68}{space 3}0.843{col 76}{space 4}  .313945{col 89}{space 3} 2.573887
{txt}{space 32}3  {c |}{col 36}{res}{space 2} .7550473{col 48}{space 2} .2188361{col 59}{space 1}   -0.97{col 68}{space 3}0.332{col 76}{space 4} .4278284{col 89}{space 3} 1.332535
{txt}{space 32}4  {c |}{col 36}{res}{space 2} .7222595{col 48}{space 2} .2342935{col 59}{space 1}   -1.00{col 68}{space 3}0.316{col 76}{space 4} .3824479{col 89}{space 3} 1.363999
{txt}{space 32}5  {c |}{col 36}{res}{space 2}  .490018{col 48}{space 2} .1502576{col 59}{space 1}   -2.33{col 68}{space 3}0.020{col 76}{space 4} .2686595{col 89}{space 3} .8937619
{txt}{space 32}6  {c |}{col 36}{res}{space 2} .5504471{col 48}{space 2} .1690669{col 59}{space 1}   -1.94{col 68}{space 3}0.052{col 76}{space 4} .3014905{col 89}{space 3} 1.004981
{txt}{space 32}7  {c |}{col 36}{res}{space 2} .4764531{col 48}{space 2} .1723919{col 59}{space 1}   -2.05{col 68}{space 3}0.040{col 76}{space 4} .2344421{col 89}{space 3} .9682885
{txt}{space 29}9999  {c |}{col 36}{res}{space 2} .6158376{col 48}{space 2}  .203146{col 59}{space 1}   -1.47{col 68}{space 3}0.142{col 76}{space 4} .3226123{col 89}{space 3} 1.175578
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-4793.444{col 39}-4505.816{col 50}    29{col 58} 9069.632{col 69} 9207.583
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelE1
{txt}
{com}. estout modelE1, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                  modelE1   
{txt}                     b/se   
{txt}{hline 28}
{txt}zloyalmedian{res}        1.358** {txt}
            {res}      (0.143)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.080   {txt}
            {res}      (0.111)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.650***{txt}
            {res}      (0.077)   {txt}
{txt}zpecompmed~n{res}        0.983   {txt}
            {res}      (0.067)   {txt}
{txt}zmecompmed~n{res}        1.006   {txt}
            {res}      (0.059)   {txt}
{txt}toplevel2   {res}        0.605***{txt}
            {res}      (0.052)   {txt}
{txt}presagency~n{res}        1.493***{txt}
            {res}      (0.135)   {txt}
{txt}presagency~d{res}        1.397***{txt}
            {res}      (0.139)   {txt}
{txt}subagencyd~n{res}        1.052   {txt}
            {res}      (0.159)   {txt}
{txt}standalone~n{res}        0.835*  {txt}
            {res}      (0.072)   {txt}
{txt}okstartsen~n{res}        0.001** {txt}
            {res}      (0.003)   {txt}
{txt}okstartfil~e{res}        1.541   {txt}
            {res}      (0.349)   {txt}
{txt}okcrossover {res}        0.184***{txt}
            {res}      (0.030)   {txt}
{txt}okstartpre~p{res}        0.995   {txt}
            {res}      (0.003)   {txt}
{txt}okstartune~t{res}        0.931   {txt}
            {res}      (0.044)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.okstarta~r{res}        1.932***{txt}
            {res}      (0.344)   {txt}
{txt}3.okstarta~r{res}        5.537***{txt}
            {res}      (0.729)   {txt}
{txt}4.okstarta~r{res}        4.634***{txt}
            {res}      (1.422)   {txt}
{txt}5.okstarta~r{res}        1.354*  {txt}
            {res}      (0.172)   {txt}
{txt}6.okstarta~r{res}        2.886***{txt}
            {res}      (0.411)   {txt}
{txt}7.okstarta~r{res}        5.425***{txt}
            {res}      (1.393)   {txt}
{txt}8.okstarta~r{res}        8.125***{txt}
            {res}      (2.393)   {txt}
{txt}1.postempl~t{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.postempl~t{res}        0.899   {txt}
            {res}      (0.482)   {txt}
{txt}3.postempl~t{res}        0.755   {txt}
            {res}      (0.219)   {txt}
{txt}4.postempl~t{res}        0.722   {txt}
            {res}      (0.234)   {txt}
{txt}5.postempl~t{res}        0.490*  {txt}
            {res}      (0.150)   {txt}
{txt}6.postempl~t{res}        0.550   {txt}
            {res}      (0.169)   {txt}
{txt}7.postempl~t{res}        0.476*  {txt}
            {res}      (0.172)   {txt}
{txt}9999.poste~t{res}        0.616   {txt}
            {res}      (0.203)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure E1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [ME1−ME4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)] ***
. 
.   
. 
. ** ONE INTERQUARTILE RANGE MARGINAL EFFECT INCREASE IN APPOINTEE LOYALTY DIFFERENTIAL BETWEEN POLICY PRIORITY AGENCY VERSUS NON-POLICY PRIORITY AGENCY [FIGURE E1] **
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5556428{col 26}{space 2} .0899211{col 37}{space 1}   -3.63{col 46}{space 3}0.000{col 54}{space 4} .4046165{col 67}{space 3} .7630409
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelE1zloyal = r(table)
{txt}
{com}. mat list modelE1zloyal
{res}
{txt}modelE1zloyal[9,1]
               (1)
     b {res}  .55564285
{txt}    se {res}  .08992109
{txt}     z {res} -3.6310964
{txt}pvalue {res}  .00028222
{txt}    ll {res}  .40461653
{txt}    ul {res}  .76304093
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. *
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. **** MODEL E2: COX MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr  i.sbagency reagan bush41 clinton bush43 i.postemployment,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
Iteration 0:   log pseudolikelihood = {res}-4793.4442
{txt}Iteration 1:   log pseudolikelihood = {res}-4492.5134
{txt}Iteration 2:   log pseudolikelihood = {res}-4465.6168
{txt}Iteration 3:   log pseudolikelihood = {res}-4465.2624
{txt}Iteration 4:   log pseudolikelihood = {res} -4465.262
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res} -4465.262

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}40{txt})    =  {res}  15988.98
{txt}Log pseudolikelihood =   {res} -4465.262             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.365498{col 48}{space 2} .1661917{col 59}{space 1}    2.56{col 68}{space 3}0.010{col 76}{space 4} 1.075705{col 89}{space 3}  1.73336
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.137725{col 48}{space 2} .2008973{col 59}{space 1}    0.73{col 68}{space 3}0.465{col 76}{space 4} .8048844{col 89}{space 3} 1.608202
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6207659{col 48}{space 2} .0919647{col 59}{space 1}   -3.22{col 68}{space 3}0.001{col 76}{space 4} .4643278{col 89}{space 3}   .82991
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.026687{col 48}{space 2} .0817827{col 59}{space 1}    0.33{col 68}{space 3}0.741{col 76}{space 4} .8782823{col 89}{space 3} 1.200169
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .9831298{col 48}{space 2} .0677347{col 59}{space 1}   -0.25{col 68}{space 3}0.805{col 76}{space 4} .8589455{col 89}{space 3} 1.125268
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .5285309{col 48}{space 2} .0628475{col 59}{space 1}   -5.36{col 68}{space 3}0.000{col 76}{space 4}  .418653{col 89}{space 3} .6672468
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .6444722{col 48}{space 2} .1828033{col 59}{space 1}   -1.55{col 68}{space 3}0.121{col 76}{space 4}  .369626{col 89}{space 3} 1.123688
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .6086303{col 48}{space 2} .1736452{col 59}{space 1}   -1.74{col 68}{space 3}0.082{col 76}{space 4} .3479378{col 89}{space 3} 1.064647
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.717781{col 48}{space 2} .3701953{col 59}{space 1}    2.51{col 68}{space 3}0.012{col 76}{space 4} 1.125971{col 89}{space 3} 2.620646
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 2.112957{col 48}{space 2} .6828686{col 59}{space 1}    2.31{col 68}{space 3}0.021{col 76}{space 4} 1.121497{col 89}{space 3} 3.980918
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 9.28e-12{col 48}{space 2} 9.85e-11{col 59}{space 1}   -2.39{col 68}{space 3}0.017{col 76}{space 4} 8.51e-21{col 89}{space 3} .0101076
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 1094.731{col 48}{space 2} 2437.851{col 59}{space 1}    3.14{col 68}{space 3}0.002{col 76}{space 4} 13.92421{col 89}{space 3} 86068.47
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2}  .157569{col 48}{space 2} .0330736{col 59}{space 1}   -8.80{col 68}{space 3}0.000{col 76}{space 4}  .104425{col 89}{space 3} .2377591
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9895561{col 48}{space 2} .0047504{col 59}{space 1}   -2.19{col 68}{space 3}0.029{col 76}{space 4} .9802892{col 89}{space 3} .9989106
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 1.140246{col 48}{space 2} .0988775{col 59}{space 1}    1.51{col 68}{space 3}0.130{col 76}{space 4} .9620235{col 89}{space 3} 1.351485
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 1.632485{col 48}{space 2} .3506704{col 59}{space 1}    2.28{col 68}{space 3}0.023{col 76}{space 4}  1.07153{col 89}{space 3} 2.487103
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 4.061359{col 48}{space 2} .8592517{col 59}{space 1}    6.62{col 68}{space 3}0.000{col 76}{space 4} 2.682778{col 89}{space 3} 6.148342
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 3.833385{col 48}{space 2} 1.209245{col 59}{space 1}    4.26{col 68}{space 3}0.000{col 76}{space 4} 2.065713{col 89}{space 3} 7.113688
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.705253{col 48}{space 2} .4195225{col 59}{space 1}    2.17{col 68}{space 3}0.030{col 76}{space 4} 1.052879{col 89}{space 3} 2.761847
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 3.937839{col 48}{space 2} .9930843{col 59}{space 1}    5.43{col 68}{space 3}0.000{col 76}{space 4} 2.402112{col 89}{space 3} 6.455392
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 6.036046{col 48}{space 2} 1.866452{col 59}{space 1}    5.81{col 68}{space 3}0.000{col 76}{space 4} 3.292654{col 89}{space 3}  11.0652
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 9.851246{col 48}{space 2}  3.70293{col 59}{space 1}    6.09{col 68}{space 3}0.000{col 76}{space 4} 4.715609{col 89}{space 3} 20.57996
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2}  3.26641{col 48}{space 2} 1.020613{col 59}{space 1}    3.79{col 68}{space 3}0.000{col 76}{space 4} 1.770544{col 89}{space 3} 6.026077
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 2.142472{col 48}{space 2}  .614534{col 59}{space 1}    2.66{col 68}{space 3}0.008{col 76}{space 4} 1.221129{col 89}{space 3}  3.75897
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 1.184609{col 48}{space 2} .3055304{col 59}{space 1}    0.66{col 68}{space 3}0.511{col 76}{space 4} .7145554{col 89}{space 3} 1.963876
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.229979{col 48}{space 2} .3543841{col 59}{space 1}    0.72{col 68}{space 3}0.472{col 76}{space 4} .6992743{col 89}{space 3} 2.163454
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.665468{col 48}{space 2} .6401368{col 59}{space 1}    4.08{col 68}{space 3}0.000{col 76}{space 4} 1.664752{col 89}{space 3} 4.267735
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 2.098049{col 48}{space 2} .6953288{col 59}{space 1}    2.24{col 68}{space 3}0.025{col 76}{space 4} 1.095754{col 89}{space 3} 4.017151
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 2.692193{col 48}{space 2} .8238946{col 59}{space 1}    3.24{col 68}{space 3}0.001{col 76}{space 4} 1.477788{col 89}{space 3} 4.904563
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 2.542957{col 48}{space 2} .7846857{col 59}{space 1}    3.02{col 68}{space 3}0.002{col 76}{space 4} 1.388935{col 89}{space 3} 4.655819
{txt}{space 31}11  {c |}{col 36}{res}{space 2} 4.356151{col 48}{space 2} 1.442181{col 59}{space 1}    4.44{col 68}{space 3}0.000{col 76}{space 4} 2.276657{col 89}{space 3} 8.335052
{txt}{space 31}12  {c |}{col 36}{res}{space 2} 1.851892{col 48}{space 2} .4054856{col 59}{space 1}    2.81{col 68}{space 3}0.005{col 76}{space 4} 1.205699{col 89}{space 3} 2.844413
{txt}{space 31}13  {c |}{col 36}{res}{space 2} 1.758836{col 48}{space 2} .4678594{col 59}{space 1}    2.12{col 68}{space 3}0.034{col 76}{space 4} 1.044242{col 89}{space 3} 2.962438
{txt}{space 31}14  {c |}{col 36}{res}{space 2} 2.913958{col 48}{space 2} .9244688{col 59}{space 1}    3.37{col 68}{space 3}0.001{col 76}{space 4} 1.564715{col 89}{space 3} 5.426647
{txt}{space 31}15  {c |}{col 36}{res}{space 2} 1.875363{col 48}{space 2} .5364926{col 59}{space 1}    2.20{col 68}{space 3}0.028{col 76}{space 4} 1.070481{col 89}{space 3} 3.285428
{txt}{space 31}16  {c |}{col 36}{res}{space 2} .8746919{col 48}{space 2} .1374837{col 59}{space 1}   -0.85{col 68}{space 3}0.394{col 76}{space 4} .6427819{col 89}{space 3} 1.190273
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 1.484227{col 48}{space 2} .2494753{col 59}{space 1}    2.35{col 68}{space 3}0.019{col 76}{space 4} 1.067644{col 89}{space 3} 2.063355
{txt}{space 31}18  {c |}{col 36}{res}{space 2} 2.284324{col 48}{space 2} .7073735{col 59}{space 1}    2.67{col 68}{space 3}0.008{col 76}{space 4} 1.245005{col 89}{space 3} 4.191258
{txt}{space 31}19  {c |}{col 36}{res}{space 2}  .757125{col 48}{space 2} .1208543{col 59}{space 1}   -1.74{col 68}{space 3}0.081{col 76}{space 4}  .553728{col 89}{space 3} 1.035234
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .2430088{col 48}{space 2} .0837837{col 59}{space 1}   -4.10{col 68}{space 3}0.000{col 76}{space 4} .1236368{col 89}{space 3} .4776352
{txt}{space 31}21  {c |}{col 36}{res}{space 2} .8833134{col 48}{space 2} .0707372{col 59}{space 1}   -1.55{col 68}{space 3}0.121{col 76}{space 4} .7550039{col 89}{space 3} 1.033428
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .4438191{col 48}{space 2} .1648497{col 59}{space 1}   -2.19{col 68}{space 3}0.029{col 76}{space 4}  .214309{col 89}{space 3} .9191184
{txt}{space 31}23  {c |}{col 36}{res}{space 2} .9139955{col 48}{space 2} .2453234{col 59}{space 1}   -0.34{col 68}{space 3}0.738{col 76}{space 4} .5401011{col 89}{space 3} 1.546725
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .2942023{col 48}{space 2} .1501557{col 59}{space 1}   -2.40{col 68}{space 3}0.017{col 76}{space 4} .1081951{col 89}{space 3} .7999899
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.439448{col 48}{space 2} .1850811{col 59}{space 1}    2.83{col 68}{space 3}0.005{col 76}{space 4} 1.118795{col 89}{space 3} 1.852003
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .8244126{col 48}{space 2} .1239267{col 59}{space 1}   -1.28{col 68}{space 3}0.199{col 76}{space 4}  .614032{col 89}{space 3} 1.106874
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} 1.692017{col 48}{space 2} .1593759{col 59}{space 1}    5.58{col 68}{space 3}0.000{col 76}{space 4} 1.406784{col 89}{space 3} 2.035081
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 3.932932{col 48}{space 2} 1.503053{col 59}{space 1}    3.58{col 68}{space 3}0.000{col 76}{space 4} 1.859567{col 89}{space 3} 8.318041
{txt}{space 31}30  {c |}{col 36}{res}{space 2} 1.528273{col 48}{space 2} .5294629{col 59}{space 1}    1.22{col 68}{space 3}0.221{col 76}{space 4} .7750083{col 89}{space 3}  3.01367
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 2.161093{col 48}{space 2} .4888819{col 59}{space 1}    3.41{col 68}{space 3}0.001{col 76}{space 4} 1.387123{col 89}{space 3} 3.366914
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 3.630996{col 48}{space 2} 1.008798{col 59}{space 1}    4.64{col 68}{space 3}0.000{col 76}{space 4}  2.10638{col 89}{space 3} 6.259142
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 1.633745{col 48}{space 2} .5177286{col 59}{space 1}    1.55{col 68}{space 3}0.121{col 76}{space 4}  .877892{col 89}{space 3} 3.040376
{txt}{space 31}53  {c |}{col 36}{res}{space 2} 1.506789{col 48}{space 2} .1606894{col 59}{space 1}    3.84{col 68}{space 3}0.000{col 76}{space 4}  1.22258{col 89}{space 3} 1.857067
{txt}{space 31}54  {c |}{col 36}{res}{space 2} 1.727757{col 48}{space 2} .3788182{col 59}{space 1}    2.49{col 68}{space 3}0.013{col 76}{space 4} 1.124224{col 89}{space 3} 2.655292
{txt}{space 31}55  {c |}{col 36}{res}{space 2} 1.289464{col 48}{space 2} .4519078{col 59}{space 1}    0.73{col 68}{space 3}0.468{col 76}{space 4} .6487774{col 89}{space 3} 2.562848
{txt}{space 31}56  {c |}{col 36}{res}{space 2} 1.071057{col 48}{space 2} .4141435{col 59}{space 1}    0.18{col 68}{space 3}0.859{col 76}{space 4} .5019729{col 89}{space 3}  2.28531
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 1.581504{col 48}{space 2} .5081536{col 59}{space 1}    1.43{col 68}{space 3}0.154{col 76}{space 4} .8425011{col 89}{space 3} 2.968727
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .3452666{col 48}{space 2} .1443718{col 59}{space 1}   -2.54{col 68}{space 3}0.011{col 76}{space 4} .1521347{col 89}{space 3} .7835755
{txt}{space 31}60  {c |}{col 36}{res}{space 2}  1.24785{col 48}{space 2} .1953226{col 59}{space 1}    1.41{col 68}{space 3}0.157{col 76}{space 4} .9181768{col 89}{space 3} 1.695894
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} .0549244{col 48}{space 2} .0502352{col 59}{space 1}   -3.17{col 68}{space 3}0.002{col 76}{space 4} .0091461{col 89}{space 3} .3298335
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2} .1538877{col 48}{space 2} .0903475{col 59}{space 1}   -3.19{col 68}{space 3}0.001{col 76}{space 4} .0486926{col 89}{space 3} .4863453
{txt}{space 27}clinton {c |}{col 36}{res}{space 2} .6877651{col 48}{space 2} .3599489{col 59}{space 1}   -0.72{col 68}{space 3}0.474{col 76}{space 4} .2465785{col 89}{space 3} 1.918338
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2} .2258959{col 48}{space 2} .1568974{col 59}{space 1}   -2.14{col 68}{space 3}0.032{col 76}{space 4}  .057903{col 89}{space 3} .8812838
{txt}{space 34} {c |}
{space 20}postemployment {c |}
{space 32}2  {c |}{col 36}{res}{space 2} .6886917{col 48}{space 2} .4508963{col 59}{space 1}   -0.57{col 68}{space 3}0.569{col 76}{space 4} .1908671{col 89}{space 3} 2.484956
{txt}{space 32}3  {c |}{col 36}{res}{space 2} .7882139{col 48}{space 2} .2810429{col 59}{space 1}   -0.67{col 68}{space 3}0.504{col 76}{space 4} .3918705{col 89}{space 3} 1.585425
{txt}{space 32}4  {c |}{col 36}{res}{space 2} .7581513{col 48}{space 2} .2624199{col 59}{space 1}   -0.80{col 68}{space 3}0.424{col 76}{space 4} .3847055{col 89}{space 3} 1.494113
{txt}{space 32}5  {c |}{col 36}{res}{space 2} .5375251{col 48}{space 2} .2019455{col 59}{space 1}   -1.65{col 68}{space 3}0.098{col 76}{space 4} .2573988{col 89}{space 3} 1.122512
{txt}{space 32}6  {c |}{col 36}{res}{space 2} .5850847{col 48}{space 2} .2141711{col 59}{space 1}   -1.46{col 68}{space 3}0.143{col 76}{space 4} .2855191{col 89}{space 3} 1.198953
{txt}{space 32}7  {c |}{col 36}{res}{space 2} .6603137{col 48}{space 2} .2734512{col 59}{space 1}   -1.00{col 68}{space 3}0.316{col 76}{space 4} .2932568{col 89}{space 3}   1.4868
{txt}{space 29}9999  {c |}{col 36}{res}{space 2} .6337346{col 48}{space 2} .2475287{col 59}{space 1}   -1.17{col 68}{space 3}0.243{col 76}{space 4} .2947399{col 89}{space 3} 1.362624
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-4793.444{col 39}-4465.262{col 50}    40{col 58} 9010.524{col 69} 9200.801
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelE2
{txt}
{com}. estout modelE2, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                  modelE2   
{txt}                     b/se   
{txt}{hline 28}
{txt}zloyalmedian{res}        1.365*  {txt}
            {res}      (0.166)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.138   {txt}
            {res}      (0.201)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.621** {txt}
            {res}      (0.092)   {txt}
{txt}zpecompmed~n{res}        1.027   {txt}
            {res}      (0.082)   {txt}
{txt}zmecompmed~n{res}        0.983   {txt}
            {res}      (0.068)   {txt}
{txt}toplevel2   {res}        0.529***{txt}
            {res}      (0.063)   {txt}
{txt}presagency~n{res}        0.644   {txt}
            {res}      (0.183)   {txt}
{txt}presagency~d{res}        0.609   {txt}
            {res}      (0.174)   {txt}
{txt}subagencyd~n{res}        1.718*  {txt}
            {res}      (0.370)   {txt}
{txt}standalone~n{res}        2.113*  {txt}
            {res}      (0.683)   {txt}
{txt}okstartsen~n{res}        0.000*  {txt}
            {res}      (0.000)   {txt}
{txt}okstartfil~e{res}     1094.731** {txt}
            {res}   (2437.851)   {txt}
{txt}okcrossover {res}        0.158***{txt}
            {res}      (0.033)   {txt}
{txt}okstartpre~p{res}        0.990*  {txt}
            {res}      (0.005)   {txt}
{txt}okstartune~t{res}        1.140   {txt}
            {res}      (0.099)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.okstarta~r{res}        1.632*  {txt}
            {res}      (0.351)   {txt}
{txt}3.okstarta~r{res}        4.061***{txt}
            {res}      (0.859)   {txt}
{txt}4.okstarta~r{res}        3.833***{txt}
            {res}      (1.209)   {txt}
{txt}5.okstarta~r{res}        1.705*  {txt}
            {res}      (0.420)   {txt}
{txt}6.okstarta~r{res}        3.938***{txt}
            {res}      (0.993)   {txt}
{txt}7.okstarta~r{res}        6.036***{txt}
            {res}      (1.866)   {txt}
{txt}8.okstarta~r{res}        9.851***{txt}
            {res}      (3.703)   {txt}
{txt}1.sbagency  {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.sbagency  {res}        3.266***{txt}
            {res}      (1.021)   {txt}
{txt}3.sbagency  {res}        2.142** {txt}
            {res}      (0.615)   {txt}
{txt}4.sbagency  {res}        1.185   {txt}
            {res}      (0.306)   {txt}
{txt}5.sbagency  {res}        1.230   {txt}
            {res}      (0.354)   {txt}
{txt}6.sbagency  {res}        2.665***{txt}
            {res}      (0.640)   {txt}
{txt}7.sbagency  {res}        2.098*  {txt}
            {res}      (0.695)   {txt}
{txt}8.sbagency  {res}        2.692** {txt}
            {res}      (0.824)   {txt}
{txt}9.sbagency  {res}        2.543** {txt}
            {res}      (0.785)   {txt}
{txt}11.sbagency {res}        4.356***{txt}
            {res}      (1.442)   {txt}
{txt}12.sbagency {res}        1.852** {txt}
            {res}      (0.405)   {txt}
{txt}13.sbagency {res}        1.759*  {txt}
            {res}      (0.468)   {txt}
{txt}14.sbagency {res}        2.914***{txt}
            {res}      (0.924)   {txt}
{txt}15.sbagency {res}        1.875*  {txt}
            {res}      (0.536)   {txt}
{txt}16.sbagency {res}        0.875   {txt}
            {res}      (0.137)   {txt}
{txt}17.sbagency {res}        1.484*  {txt}
            {res}      (0.249)   {txt}
{txt}18.sbagency {res}        2.284** {txt}
            {res}      (0.707)   {txt}
{txt}19.sbagency {res}        0.757   {txt}
            {res}      (0.121)   {txt}
{txt}20.sbagency {res}        0.243***{txt}
            {res}      (0.084)   {txt}
{txt}21.sbagency {res}        0.883   {txt}
            {res}      (0.071)   {txt}
{txt}22.sbagency {res}        0.444*  {txt}
            {res}      (0.165)   {txt}
{txt}23.sbagency {res}        0.914   {txt}
            {res}      (0.245)   {txt}
{txt}24.sbagency {res}        0.294*  {txt}
            {res}      (0.150)   {txt}
{txt}25.sbagency {res}        1.439** {txt}
            {res}      (0.185)   {txt}
{txt}26.sbagency {res}        0.824   {txt}
            {res}      (0.124)   {txt}
{txt}27.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}28.sbagency {res}        1.692***{txt}
            {res}      (0.159)   {txt}
{txt}29.sbagency {res}        3.933***{txt}
            {res}      (1.503)   {txt}
{txt}30.sbagency {res}        1.528   {txt}
            {res}      (0.529)   {txt}
{txt}50.sbagency {res}        2.161***{txt}
            {res}      (0.489)   {txt}
{txt}51.sbagency {res}        3.631***{txt}
            {res}      (1.009)   {txt}
{txt}52.sbagency {res}        1.634   {txt}
            {res}      (0.518)   {txt}
{txt}53.sbagency {res}        1.507***{txt}
            {res}      (0.161)   {txt}
{txt}54.sbagency {res}        1.728*  {txt}
            {res}      (0.379)   {txt}
{txt}55.sbagency {res}        1.289   {txt}
            {res}      (0.452)   {txt}
{txt}56.sbagency {res}        1.071   {txt}
            {res}      (0.414)   {txt}
{txt}57.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}58.sbagency {res}        1.582   {txt}
            {res}      (0.508)   {txt}
{txt}59.sbagency {res}        0.345*  {txt}
            {res}      (0.144)   {txt}
{txt}60.sbagency {res}        1.248   {txt}
            {res}      (0.195)   {txt}
{txt}61.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}reagan      {res}        0.055** {txt}
            {res}      (0.050)   {txt}
{txt}bush41      {res}        0.154** {txt}
            {res}      (0.090)   {txt}
{txt}clinton     {res}        0.688   {txt}
            {res}      (0.360)   {txt}
{txt}bush43      {res}        0.226*  {txt}
            {res}      (0.157)   {txt}
{txt}1.postempl~t{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.postempl~t{res}        0.689   {txt}
            {res}      (0.451)   {txt}
{txt}3.postempl~t{res}        0.788   {txt}
            {res}      (0.281)   {txt}
{txt}4.postempl~t{res}        0.758   {txt}
            {res}      (0.262)   {txt}
{txt}5.postempl~t{res}        0.538   {txt}
            {res}      (0.202)   {txt}
{txt}6.postempl~t{res}        0.585   {txt}
            {res}      (0.214)   {txt}
{txt}7.postempl~t{res}        0.660   {txt}
            {res}      (0.273)   {txt}
{txt}9999.poste~t{res}        0.634   {txt}
            {res}      (0.248)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure E1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [ME1−ME4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]  ***
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5215304{col 26}{space 2} .1054893{col 37}{space 1}   -3.22{col 46}{space 3}0.001{col 54}{space 4} .3508392{col 67}{space 3} .7752668
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelE2zloyal = r(table)
{txt}
{com}. mat list modelE2zloyal
{res}
{txt}modelE2zloyal[9,1]
               (1)
     b {res}   .5215304
{txt}    se {res}  .10548934
{txt}     z {res} -3.2184284
{txt}pvalue {res}  .00128895
{txt}    ll {res}  .35083919
{txt}    ul {res}  .77526675
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** MODEL E3: WEIBULL MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.postemployment,   distribution(weibull)  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:  -616.246}  
Iteration 2:{space 3}log pseudolikelihood = {res:-532.32416}  
Iteration 3:{space 3}log pseudolikelihood = {res:-531.23536}  
Iteration 4:{space 3}log pseudolikelihood = {res:-531.23414}  
Iteration 5:{space 3}log pseudolikelihood = {res:-531.23414}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}29{txt})    =  {res}   5031.20
{txt}Log pseudolikelihood =   {res}-531.23414             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.376297{col 48}{space 2} .1507873{col 59}{space 1}    2.92{col 68}{space 3}0.004{col 76}{space 4} 1.110336{col 89}{space 3} 1.705965
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.102681{col 48}{space 2} .1164059{col 59}{space 1}    0.93{col 68}{space 3}0.354{col 76}{space 4} .8965859{col 89}{space 3} 1.356151
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6420423{col 48}{space 2} .0783736{col 59}{space 1}   -3.63{col 68}{space 3}0.000{col 76}{space 4} .5054266{col 89}{space 3} .8155849
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} .9886343{col 48}{space 2} .0684569{col 59}{space 1}   -0.17{col 68}{space 3}0.869{col 76}{space 4} .8631676{col 89}{space 3} 1.132338
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} 1.008902{col 48}{space 2} .0576874{col 59}{space 1}    0.15{col 68}{space 3}0.877{col 76}{space 4} .9019417{col 89}{space 3} 1.128546
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .6240627{col 48}{space 2} .0544807{col 59}{space 1}   -5.40{col 68}{space 3}0.000{col 76}{space 4} .5259184{col 89}{space 3} .7405222
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} 1.508576{col 48}{space 2} .1364889{col 59}{space 1}    4.54{col 68}{space 3}0.000{col 76}{space 4} 1.263439{col 89}{space 3} 1.801275
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} 1.399872{col 48}{space 2} .1367791{col 59}{space 1}    3.44{col 68}{space 3}0.001{col 76}{space 4} 1.155897{col 89}{space 3} 1.695344
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.052448{col 48}{space 2} .1613204{col 59}{space 1}    0.33{col 68}{space 3}0.739{col 76}{space 4} .7793414{col 89}{space 3} 1.421261
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} .8180317{col 48}{space 2} .0680456{col 59}{space 1}   -2.41{col 68}{space 3}0.016{col 76}{space 4}  .694969{col 89}{space 3}  .962886
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} .0009631{col 48}{space 2} .0025474{col 59}{space 1}   -2.63{col 68}{space 3}0.009{col 76}{space 4} 5.40e-06{col 89}{space 3} .1718036
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 1.694122{col 48}{space 2} .3941855{col 59}{space 1}    2.27{col 68}{space 3}0.023{col 76}{space 4} 1.073713{col 89}{space 3} 2.673011
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .1908089{col 48}{space 2} .0321542{col 59}{space 1}   -9.83{col 68}{space 3}0.000{col 76}{space 4} .1371381{col 89}{space 3} .2654845
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9956354{col 48}{space 2} .0034104{col 59}{space 1}   -1.28{col 68}{space 3}0.202{col 76}{space 4} .9889736{col 89}{space 3} 1.002342
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} .9315433{col 48}{space 2} .0434557{col 59}{space 1}   -1.52{col 68}{space 3}0.128{col 76}{space 4} .8501493{col 89}{space 3}  1.02073
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 1.910352{col 48}{space 2}  .340566{col 59}{space 1}    3.63{col 68}{space 3}0.000{col 76}{space 4} 1.346996{col 89}{space 3} 2.709321
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 6.038868{col 48}{space 2}   .77962{col 59}{space 1}   13.93{col 68}{space 3}0.000{col 76}{space 4} 4.688837{col 89}{space 3} 7.777607
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 5.132046{col 48}{space 2} 1.476492{col 59}{space 1}    5.68{col 68}{space 3}0.000{col 76}{space 4} 2.920113{col 89}{space 3} 9.019481
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.331235{col 48}{space 2} .1819591{col 59}{space 1}    2.09{col 68}{space 3}0.036{col 76}{space 4} 1.018377{col 89}{space 3} 1.740206
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.804335{col 48}{space 2} .3672637{col 59}{space 1}    7.87{col 68}{space 3}0.000{col 76}{space 4} 2.169472{col 89}{space 3}  3.62498
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 6.070221{col 48}{space 2} 1.453659{col 59}{space 1}    7.53{col 68}{space 3}0.000{col 76}{space 4} 3.796331{col 89}{space 3} 9.706104
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 9.365712{col 48}{space 2} 2.654784{col 59}{space 1}    7.89{col 68}{space 3}0.000{col 76}{space 4} 5.373548{col 89}{space 3} 16.32377
{txt}{space 34} {c |}
{space 20}postemployment {c |}
{space 32}2  {c |}{col 36}{res}{space 2} .9843712{col 48}{space 2} .5342237{col 59}{space 1}   -0.03{col 68}{space 3}0.977{col 76}{space 4} .3397872{col 89}{space 3} 2.851745
{txt}{space 32}3  {c |}{col 36}{res}{space 2} .7536672{col 48}{space 2} .2233067{col 59}{space 1}   -0.95{col 68}{space 3}0.340{col 76}{space 4} .4216714{col 89}{space 3} 1.347054
{txt}{space 32}4  {c |}{col 36}{res}{space 2} .7183062{col 48}{space 2} .2351534{col 59}{space 1}   -1.01{col 68}{space 3}0.312{col 76}{space 4} .3781378{col 89}{space 3} 1.364486
{txt}{space 32}5  {c |}{col 36}{res}{space 2} .4876068{col 48}{space 2} .1509534{col 59}{space 1}   -2.32{col 68}{space 3}0.020{col 76}{space 4} .2657998{col 89}{space 3} .8945092
{txt}{space 32}6  {c |}{col 36}{res}{space 2} .5503196{col 48}{space 2} .1692599{col 59}{space 1}   -1.94{col 68}{space 3}0.052{col 76}{space 4} .3011714{col 89}{space 3} 1.005579
{txt}{space 32}7  {c |}{col 36}{res}{space 2} .4480088{col 48}{space 2} .1660966{col 59}{space 1}   -2.17{col 68}{space 3}0.030{col 76}{space 4} .2166251{col 89}{space 3} .9265405
{txt}{space 29}9999  {c |}{col 36}{res}{space 2} .6242014{col 48}{space 2}     .206{col 59}{space 1}   -1.43{col 68}{space 3}0.153{col 76}{space 4} .3268962{col 89}{space 3}   1.1919
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} 3.21e-06{col 48}{space 2} 6.31e-06{col 59}{space 1}   -6.44{col 68}{space 3}0.000{col 76}{space 4} 6.86e-08{col 89}{space 3} .0001507
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29}/ln_p {c |}{col 36}{res}{space 2} .9298255{col 48}{space 2} .0304495{col 59}{space 1}   30.54{col 68}{space 3}0.000{col 76}{space 4} .8701456{col 89}{space 3} .9895054
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                 p {c |}{col 36}{res}{space 2} 2.534067{col 48}{space 2}  .077161{col 76}{space 4} 2.387258{col 89}{space 3} 2.689904
{txt}                               1/p {c |}{col 36}{res}{space 2} .3946226{col 48}{space 2} .0120161{col 76}{space 4} .3717605{col 89}{space 3} .4188905
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-830.8551{col 39}-531.2341{col 50}    31{col 58} 1124.468{col 69} 1271.933
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelE3
{txt}
{com}. estout modelE3, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                  modelE3   
{txt}                     b/se   
{txt}{hline 28}
{res}_t                          {txt}
{txt}zloyalmedian{res}        1.376** {txt}
            {res}      (0.151)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.103   {txt}
            {res}      (0.116)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.642***{txt}
            {res}      (0.078)   {txt}
{txt}zpecompmed~n{res}        0.989   {txt}
            {res}      (0.068)   {txt}
{txt}zmecompmed~n{res}        1.009   {txt}
            {res}      (0.058)   {txt}
{txt}toplevel2   {res}        0.624***{txt}
            {res}      (0.054)   {txt}
{txt}presagency~n{res}        1.509***{txt}
            {res}      (0.136)   {txt}
{txt}presagency~d{res}        1.400***{txt}
            {res}      (0.137)   {txt}
{txt}subagencyd~n{res}        1.052   {txt}
            {res}      (0.161)   {txt}
{txt}standalone~n{res}        0.818*  {txt}
            {res}      (0.068)   {txt}
{txt}okstartsen~n{res}        0.001** {txt}
            {res}      (0.003)   {txt}
{txt}okstartfil~e{res}        1.694*  {txt}
            {res}      (0.394)   {txt}
{txt}okcrossover {res}        0.191***{txt}
            {res}      (0.032)   {txt}
{txt}okstartpre~p{res}        0.996   {txt}
            {res}      (0.003)   {txt}
{txt}okstartune~t{res}        0.932   {txt}
            {res}      (0.043)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.okstarta~r{res}        1.910***{txt}
            {res}      (0.341)   {txt}
{txt}3.okstarta~r{res}        6.039***{txt}
            {res}      (0.780)   {txt}
{txt}4.okstarta~r{res}        5.132***{txt}
            {res}      (1.476)   {txt}
{txt}5.okstarta~r{res}        1.331*  {txt}
            {res}      (0.182)   {txt}
{txt}6.okstarta~r{res}        2.804***{txt}
            {res}      (0.367)   {txt}
{txt}7.okstarta~r{res}        6.070***{txt}
            {res}      (1.454)   {txt}
{txt}8.okstarta~r{res}        9.366***{txt}
            {res}      (2.655)   {txt}
{txt}1.postempl~t{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.postempl~t{res}        0.984   {txt}
            {res}      (0.534)   {txt}
{txt}3.postempl~t{res}        0.754   {txt}
            {res}      (0.223)   {txt}
{txt}4.postempl~t{res}        0.718   {txt}
            {res}      (0.235)   {txt}
{txt}5.postempl~t{res}        0.488*  {txt}
            {res}      (0.151)   {txt}
{txt}6.postempl~t{res}        0.550   {txt}
            {res}      (0.169)   {txt}
{txt}7.postempl~t{res}        0.448*  {txt}
            {res}      (0.166)   {txt}
{txt}9999.poste~t{res}        0.624   {txt}
            {res}      (0.206)   {txt}
{txt}_cons       {res}        0.000***{txt}
            {res}      (0.000)   {txt}
{txt}{hline 28}
{res}/                           {txt}
{txt}ln_p        {res}        2.534***{txt}
            {res}      (0.077)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure E1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [ME1−ME4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)] ***
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5460875{col 26}{space 2} .0910131{col 37}{space 1}   -3.63{col 46}{space 3}0.000{col 54}{space 4} .3939105{col 67}{space 3}  .757054
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelE3zloyal = r(table)
{txt}
{com}. mat list modelE3zloyal
{res}
{txt}modelE3zloyal[9,1]
               (1)
     b {res}  .54608748
{txt}    se {res}  .09101311
{txt}     z {res} -3.6299154
{txt}pvalue {res}  .00028351
{txt}    ll {res}  .39391054
{txt}    ul {res}  .75705398
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. **** COMPUTE Figure E1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [ME1−ME4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)] ***
. 
. ** Generate 'manual' interaction variable ** 
. generate loyalppdiff = soubinaryagency2nom*zloyalmedian
{txt}
{com}. 
. ** Re-Estimate Model E2  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.postemployment, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:  -616.246}  
Iteration 2:{space 3}log pseudolikelihood = {res:-532.32416}  
Iteration 3:{space 3}log pseudolikelihood = {res:-531.23536}  
Iteration 4:{space 3}log pseudolikelihood = {res:-531.23414}  
Iteration 5:{space 3}log pseudolikelihood = {res:-531.23414}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}29{txt})    =  {res}   5031.20
{txt}Log pseudolikelihood =   {res}-531.23414             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.376297{col 40}{space 2} .1507873{col 51}{space 1}    2.92{col 60}{space 3}0.004{col 68}{space 4} 1.110336{col 81}{space 3} 1.705965
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.102681{col 40}{space 2} .1164059{col 51}{space 1}    0.93{col 60}{space 3}0.354{col 68}{space 4} .8965859{col 81}{space 3} 1.356151
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .6420423{col 40}{space 2} .0783736{col 51}{space 1}   -3.63{col 60}{space 3}0.000{col 68}{space 4} .5054266{col 81}{space 3} .8155849
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} .9886343{col 40}{space 2} .0684569{col 51}{space 1}   -0.17{col 60}{space 3}0.869{col 68}{space 4} .8631676{col 81}{space 3} 1.132338
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} 1.008902{col 40}{space 2} .0576874{col 51}{space 1}    0.15{col 60}{space 3}0.877{col 68}{space 4} .9019417{col 81}{space 3} 1.128546
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .6240627{col 40}{space 2} .0544807{col 51}{space 1}   -5.40{col 60}{space 3}0.000{col 68}{space 4} .5259184{col 81}{space 3} .7405222
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} 1.508576{col 40}{space 2} .1364889{col 51}{space 1}    4.54{col 60}{space 3}0.000{col 68}{space 4} 1.263439{col 81}{space 3} 1.801275
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} 1.399872{col 40}{space 2} .1367791{col 51}{space 1}    3.44{col 60}{space 3}0.001{col 68}{space 4} 1.155897{col 81}{space 3} 1.695344
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.052448{col 40}{space 2} .1613204{col 51}{space 1}    0.33{col 60}{space 3}0.739{col 68}{space 4} .7793414{col 81}{space 3} 1.421261
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} .8180317{col 40}{space 2} .0680456{col 51}{space 1}   -2.41{col 60}{space 3}0.016{col 68}{space 4}  .694969{col 81}{space 3}  .962886
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} .0009631{col 40}{space 2} .0025474{col 51}{space 1}   -2.63{col 60}{space 3}0.009{col 68}{space 4} 5.40e-06{col 81}{space 3} .1718036
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 1.694122{col 40}{space 2} .3941855{col 51}{space 1}    2.27{col 60}{space 3}0.023{col 68}{space 4} 1.073713{col 81}{space 3} 2.673011
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .1908089{col 40}{space 2} .0321542{col 51}{space 1}   -9.83{col 60}{space 3}0.000{col 68}{space 4} .1371381{col 81}{space 3} .2654845
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9956354{col 40}{space 2} .0034104{col 51}{space 1}   -1.28{col 60}{space 3}0.202{col 68}{space 4} .9889736{col 81}{space 3} 1.002342
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} .9315433{col 40}{space 2} .0434557{col 51}{space 1}   -1.52{col 60}{space 3}0.128{col 68}{space 4} .8501493{col 81}{space 3}  1.02073
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.910352{col 40}{space 2}  .340566{col 51}{space 1}    3.63{col 60}{space 3}0.000{col 68}{space 4} 1.346996{col 81}{space 3} 2.709321
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 6.038868{col 40}{space 2}   .77962{col 51}{space 1}   13.93{col 60}{space 3}0.000{col 68}{space 4} 4.688837{col 81}{space 3} 7.777607
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 5.132046{col 40}{space 2} 1.476492{col 51}{space 1}    5.68{col 60}{space 3}0.000{col 68}{space 4} 2.920113{col 81}{space 3} 9.019481
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.331235{col 40}{space 2} .1819591{col 51}{space 1}    2.09{col 60}{space 3}0.036{col 68}{space 4} 1.018377{col 81}{space 3} 1.740206
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.804335{col 40}{space 2} .3672637{col 51}{space 1}    7.87{col 60}{space 3}0.000{col 68}{space 4} 2.169472{col 81}{space 3}  3.62498
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 6.070221{col 40}{space 2} 1.453659{col 51}{space 1}    7.53{col 60}{space 3}0.000{col 68}{space 4} 3.796331{col 81}{space 3} 9.706104
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 9.365712{col 40}{space 2} 2.654784{col 51}{space 1}    7.89{col 60}{space 3}0.000{col 68}{space 4} 5.373548{col 81}{space 3} 16.32377
{txt}{space 26} {c |}
{space 12}postemployment {c |}
{space 24}2  {c |}{col 28}{res}{space 2} .9843712{col 40}{space 2} .5342237{col 51}{space 1}   -0.03{col 60}{space 3}0.977{col 68}{space 4} .3397872{col 81}{space 3} 2.851745
{txt}{space 24}3  {c |}{col 28}{res}{space 2} .7536672{col 40}{space 2} .2233067{col 51}{space 1}   -0.95{col 60}{space 3}0.340{col 68}{space 4} .4216714{col 81}{space 3} 1.347054
{txt}{space 24}4  {c |}{col 28}{res}{space 2} .7183062{col 40}{space 2} .2351534{col 51}{space 1}   -1.01{col 60}{space 3}0.312{col 68}{space 4} .3781378{col 81}{space 3} 1.364486
{txt}{space 24}5  {c |}{col 28}{res}{space 2} .4876068{col 40}{space 2} .1509534{col 51}{space 1}   -2.32{col 60}{space 3}0.020{col 68}{space 4} .2657998{col 81}{space 3} .8945092
{txt}{space 24}6  {c |}{col 28}{res}{space 2} .5503196{col 40}{space 2} .1692599{col 51}{space 1}   -1.94{col 60}{space 3}0.052{col 68}{space 4} .3011714{col 81}{space 3} 1.005579
{txt}{space 24}7  {c |}{col 28}{res}{space 2} .4480088{col 40}{space 2} .1660966{col 51}{space 1}   -2.17{col 60}{space 3}0.030{col 68}{space 4} .2166251{col 81}{space 3} .9265405
{txt}{space 21}9999  {c |}{col 28}{res}{space 2} .6242014{col 40}{space 2}     .206{col 51}{space 1}   -1.43{col 60}{space 3}0.153{col 68}{space 4} .3268962{col 81}{space 3}   1.1919
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 3.21e-06{col 40}{space 2} 6.31e-06{col 51}{space 1}   -6.44{col 60}{space 3}0.000{col 68}{space 4} 6.86e-08{col 81}{space 3} .0001507
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} .9298255{col 40}{space 2} .0304495{col 51}{space 1}   30.54{col 60}{space 3}0.000{col 68}{space 4} .8701456{col 81}{space 3} .9895054
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.534067{col 40}{space 2}  .077161{col 68}{space 4} 2.387258{col 81}{space 3} 2.689904
{txt}                       1/p {c |}{col 28}{res}{space 2} .3946226{col 40}{space 2} .0120161{col 68}{space 4} .3717605{col 81}{space 3} .4188905
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimate store modelE2a
{txt}
{com}. 
. 
. margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 910.1738{col 26}{space 2}  24.2283{col 37}{space 1}   37.57{col 46}{space 3}0.000{col 54}{space 4} 862.6872{col 67}{space 3} 957.6604
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1155.597{col 26}{space 2} 54.37941{col 37}{space 1}   21.25{col 46}{space 3}0.000{col 54}{space 4} 1049.016{col 67}{space 3} 1262.179
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}    11.76{col 38}{space 2}   0.0006
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 245.4237{col 26}{space 2} 71.56771{col 37}{space 5} 105.1535{col 51}{space 3} 385.6938
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. matrix modelE3azloyal = r(table)
{txt}
{com}. mat list modelE3azloyal
{res}
{txt}modelE3azloyal[9,1]
            r2vs1.
              _at
     b {res} 245.42367
{txt}    se {res} 71.567713
{txt}     z {res} 3.4292512
{txt}pvalue {res} .00060525
{txt}    ll {res} 105.15353
{txt}    ul {res} 385.69381
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. estimates restore modelE2a
{txt}(results {stata estimates replay modelE2a:modelE2a} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 871.3762{col 26}{space 2} 32.46587{col 37}{space 1}   26.84{col 46}{space 3}0.000{col 54}{space 4} 807.7442{col 67}{space 3} 935.0081
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1315.705{col 26}{space 2} 108.0432{col 37}{space 1}   12.18{col 46}{space 3}0.000{col 54}{space 4} 1103.944{col 67}{space 3} 1527.466
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}    10.67{col 38}{space 2}   0.0011
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2}  444.329{col 26}{space 2} 136.0555{col 37}{space 5}  177.665{col 51}{space 3} 710.9929
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelE3bzloyal = r(table)
{txt}
{com}. mat list modelE3bzloyal
{res}
{txt}modelE3bzloyal[9,1]
            r2vs1.
              _at
     b {res} 444.32898
{txt}    se {res} 136.05554
{txt}     z {res} 3.2657911
{txt}pvalue {res} .00109159
{txt}    ll {res} 177.66502
{txt}    ul {res} 710.99294
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. **** MODEL E4: WEIBULL MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.postemployment  i.sbagency reagan bush41 clinton bush43 , distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-601.73178}  
Iteration 2:{space 3}log pseudolikelihood = {res:-493.59776}  
Iteration 3:{space 3}log pseudolikelihood = {res:-492.27816}  
Iteration 4:{space 3}log pseudolikelihood = {res:-492.27527}  
Iteration 5:{space 3}log pseudolikelihood = {res:-492.27527}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
{col 49}{help j_robustsingular##|_new:Wald chi2(29)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-492.27527             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.353125{col 48}{space 2} .1664157{col 59}{space 1}    2.46{col 68}{space 3}0.014{col 76}{space 4} 1.063291{col 89}{space 3} 1.721964
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.139224{col 48}{space 2} .2081116{col 59}{space 1}    0.71{col 68}{space 3}0.476{col 76}{space 4} .7963663{col 89}{space 3} 1.629691
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6300238{col 48}{space 2} .0927611{col 59}{space 1}   -3.14{col 68}{space 3}0.002{col 76}{space 4} .4720967{col 89}{space 3} .8407811
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.033273{col 48}{space 2} .0807359{col 59}{space 1}    0.42{col 68}{space 3}0.675{col 76}{space 4} .8865552{col 89}{space 3} 1.204273
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .9899278{col 48}{space 2}  .066954{col 59}{space 1}   -0.15{col 68}{space 3}0.881{col 76}{space 4} .8670263{col 89}{space 3} 1.130251
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .5601219{col 48}{space 2}  .065567{col 59}{space 1}   -4.95{col 68}{space 3}0.000{col 76}{space 4} .4452892{col 89}{space 3} .7045679
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .7273595{col 48}{space 2} .2004698{col 59}{space 1}   -1.16{col 68}{space 3}0.248{col 76}{space 4} .4237861{col 89}{space 3} 1.248393
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .6791953{col 48}{space 2}   .18798{col 59}{space 1}   -1.40{col 68}{space 3}0.162{col 76}{space 4} .3948287{col 89}{space 3} 1.168371
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.639018{col 48}{space 2} .3564941{col 59}{space 1}    2.27{col 68}{space 3}0.023{col 76}{space 4} 1.070147{col 89}{space 3} 2.510291
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 1.793328{col 48}{space 2} .5608521{col 59}{space 1}    1.87{col 68}{space 3}0.062{col 76}{space 4} .9715202{col 89}{space 3} 3.310303
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 4.72e-11{col 48}{space 2} 4.91e-10{col 59}{space 1}   -2.29{col 68}{space 3}0.022{col 76}{space 4} 6.66e-20{col 89}{space 3} .0334842
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 854.2056{col 48}{space 2} 1867.374{col 59}{space 1}    3.09{col 68}{space 3}0.002{col 76}{space 4} 11.76943{col 89}{space 3} 61996.83
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .1674713{col 48}{space 2} .0344922{col 59}{space 1}   -8.68{col 68}{space 3}0.000{col 76}{space 4} .1118479{col 89}{space 3}  .250757
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2}  .990001{col 48}{space 2} .0046788{col 59}{space 1}   -2.13{col 68}{space 3}0.033{col 76}{space 4} .9808732{col 89}{space 3} .9992139
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 1.131843{col 48}{space 2} .0974701{col 59}{space 1}    1.44{col 68}{space 3}0.150{col 76}{space 4} .9560569{col 89}{space 3}  1.33995
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2}  1.67478{col 48}{space 2}  .347953{col 59}{space 1}    2.48{col 68}{space 3}0.013{col 76}{space 4} 1.114581{col 89}{space 3} 2.516539
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 4.488338{col 48}{space 2}  .896839{col 59}{space 1}    7.51{col 68}{space 3}0.000{col 76}{space 4} 3.033908{col 89}{space 3}  6.64001
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 4.262204{col 48}{space 2} 1.247245{col 59}{space 1}    4.95{col 68}{space 3}0.000{col 76}{space 4}  2.40186{col 89}{space 3} 7.563462
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.578466{col 48}{space 2} .3897012{col 59}{space 1}    1.85{col 68}{space 3}0.064{col 76}{space 4} .9729384{col 89}{space 3} 2.560855
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 3.643104{col 48}{space 2} .9121521{col 59}{space 1}    5.16{col 68}{space 3}0.000{col 76}{space 4}  2.23023{col 89}{space 3} 5.951048
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 6.692187{col 48}{space 2}  1.99788{col 59}{space 1}    6.37{col 68}{space 3}0.000{col 76}{space 4} 3.727787{col 89}{space 3} 12.01393
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 10.89763{col 48}{space 2} 4.082211{col 59}{space 1}    6.38{col 68}{space 3}0.000{col 76}{space 4} 5.229678{col 89}{space 3} 22.70852
{txt}{space 34} {c |}
{space 20}postemployment {c |}
{space 32}2  {c |}{col 36}{res}{space 2} .7113708{col 48}{space 2} .4560284{col 59}{space 1}   -0.53{col 68}{space 3}0.595{col 76}{space 4} .2025015{col 89}{space 3} 2.498986
{txt}{space 32}3  {c |}{col 36}{res}{space 2} .7558988{col 48}{space 2} .2639055{col 59}{space 1}   -0.80{col 68}{space 3}0.423{col 76}{space 4} .3813162{col 89}{space 3} 1.498449
{txt}{space 32}4  {c |}{col 36}{res}{space 2} .7339528{col 48}{space 2} .2509247{col 59}{space 1}   -0.90{col 68}{space 3}0.366{col 76}{space 4} .3755418{col 89}{space 3} 1.434426
{txt}{space 32}5  {c |}{col 36}{res}{space 2} .5185161{col 48}{space 2} .1918947{col 59}{space 1}   -1.77{col 68}{space 3}0.076{col 76}{space 4} .2510417{col 89}{space 3} 1.070973
{txt}{space 32}6  {c |}{col 36}{res}{space 2} .5714293{col 48}{space 2} .2021806{col 59}{space 1}   -1.58{col 68}{space 3}0.114{col 76}{space 4} .2856236{col 89}{space 3} 1.143223
{txt}{space 32}7  {c |}{col 36}{res}{space 2} .6195403{col 48}{space 2} .2468016{col 59}{space 1}   -1.20{col 68}{space 3}0.229{col 76}{space 4} .2837807{col 89}{space 3} 1.352559
{txt}{space 29}9999  {c |}{col 36}{res}{space 2} .6163385{col 48}{space 2} .2372457{col 59}{space 1}   -1.26{col 68}{space 3}0.209{col 76}{space 4} .2898467{col 89}{space 3}   1.3106
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 2.825678{col 48}{space 2} .8495995{col 59}{space 1}    3.45{col 68}{space 3}0.001{col 76}{space 4} 1.567441{col 89}{space 3} 5.093946
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 1.870124{col 48}{space 2} .5305185{col 59}{space 1}    2.21{col 68}{space 3}0.027{col 76}{space 4} 1.072509{col 89}{space 3} 3.260919
{txt}{space 32}4  {c |}{col 36}{res}{space 2}  1.06328{col 48}{space 2} .2554927{col 59}{space 1}    0.26{col 68}{space 3}0.798{col 76}{space 4} .6639182{col 89}{space 3} 1.702866
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.097973{col 48}{space 2} .2959438{col 59}{space 1}    0.35{col 68}{space 3}0.729{col 76}{space 4} .6473837{col 89}{space 3} 1.862179
{txt}{space 32}6  {c |}{col 36}{res}{space 2}  2.25944{col 48}{space 2} .5262711{col 59}{space 1}    3.50{col 68}{space 3}0.000{col 76}{space 4} 1.431324{col 89}{space 3} 3.566675
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 1.879225{col 48}{space 2} .6077298{col 59}{space 1}    1.95{col 68}{space 3}0.051{col 76}{space 4} .9970234{col 89}{space 3} 3.542029
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 2.349701{col 48}{space 2} .6877356{col 59}{space 1}    2.92{col 68}{space 3}0.004{col 76}{space 4} 1.323957{col 89}{space 3} 4.170147
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 2.280259{col 48}{space 2} .6802051{col 59}{space 1}    2.76{col 68}{space 3}0.006{col 76}{space 4} 1.270777{col 89}{space 3} 4.091655
{txt}{space 31}11  {c |}{col 36}{res}{space 2} 3.654478{col 48}{space 2} 1.191016{col 59}{space 1}    3.98{col 68}{space 3}0.000{col 76}{space 4} 1.929361{col 89}{space 3} 6.922086
{txt}{space 31}12  {c |}{col 36}{res}{space 2} 1.693453{col 48}{space 2} .3628639{col 59}{space 1}    2.46{col 68}{space 3}0.014{col 76}{space 4} 1.112711{col 89}{space 3} 2.577294
{txt}{space 31}13  {c |}{col 36}{res}{space 2}  1.54658{col 48}{space 2} .4004922{col 59}{space 1}    1.68{col 68}{space 3}0.092{col 76}{space 4} .9310025{col 89}{space 3} 2.569176
{txt}{space 31}14  {c |}{col 36}{res}{space 2} 2.443826{col 48}{space 2} .7572283{col 59}{space 1}    2.88{col 68}{space 3}0.004{col 76}{space 4} 1.331443{col 89}{space 3} 4.485576
{txt}{space 31}15  {c |}{col 36}{res}{space 2} 1.697156{col 48}{space 2} .4687935{col 59}{space 1}    1.91{col 68}{space 3}0.055{col 76}{space 4}  .987644{col 89}{space 3} 2.916374
{txt}{space 31}16  {c |}{col 36}{res}{space 2} .8566316{col 48}{space 2}   .14148{col 59}{space 1}   -0.94{col 68}{space 3}0.349{col 76}{space 4}  .619742{col 89}{space 3}  1.18407
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 1.448179{col 48}{space 2} .2374233{col 59}{space 1}    2.26{col 68}{space 3}0.024{col 76}{space 4} 1.050198{col 89}{space 3} 1.996979
{txt}{space 31}18  {c |}{col 36}{res}{space 2}  1.99726{col 48}{space 2} .5946542{col 59}{space 1}    2.32{col 68}{space 3}0.020{col 76}{space 4}   1.1143{col 89}{space 3} 3.579869
{txt}{space 31}19  {c |}{col 36}{res}{space 2} .7490588{col 48}{space 2} .1199849{col 59}{space 1}   -1.80{col 68}{space 3}0.071{col 76}{space 4} .5472297{col 89}{space 3} 1.025326
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .2897999{col 48}{space 2}  .093347{col 59}{space 1}   -3.85{col 68}{space 3}0.000{col 76}{space 4} .1541413{col 89}{space 3} .5448506
{txt}{space 31}21  {c |}{col 36}{res}{space 2} .9109079{col 48}{space 2}  .069936{col 59}{space 1}   -1.22{col 68}{space 3}0.224{col 76}{space 4} .7836506{col 89}{space 3} 1.058831
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .4827715{col 48}{space 2} .1670143{col 59}{space 1}   -2.10{col 68}{space 3}0.035{col 76}{space 4} .2450583{col 89}{space 3}  .951073
{txt}{space 31}23  {c |}{col 36}{res}{space 2} 1.025106{col 48}{space 2} .2781774{col 59}{space 1}    0.09{col 68}{space 3}0.927{col 76}{space 4} .6022581{col 89}{space 3} 1.744836
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .3216964{col 48}{space 2} .1409877{col 59}{space 1}   -2.59{col 68}{space 3}0.010{col 76}{space 4} .1362686{col 89}{space 3} .7594455
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.502735{col 48}{space 2} .1972936{col 59}{space 1}    3.10{col 68}{space 3}0.002{col 76}{space 4} 1.161792{col 89}{space 3} 1.943732
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .8284187{col 48}{space 2} .1315719{col 59}{space 1}   -1.19{col 68}{space 3}0.236{col 76}{space 4} .6068195{col 89}{space 3} 1.130942
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} 1.490238{col 48}{space 2} .1440611{col 59}{space 1}    4.13{col 68}{space 3}0.000{col 76}{space 4} 1.233019{col 89}{space 3} 1.801114
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 3.280295{col 48}{space 2} 1.189751{col 59}{space 1}    3.28{col 68}{space 3}0.001{col 76}{space 4} 1.611333{col 89}{space 3} 6.677912
{txt}{space 31}30  {c |}{col 36}{res}{space 2} 1.339043{col 48}{space 2} .4574451{col 59}{space 1}    0.85{col 68}{space 3}0.393{col 76}{space 4} .6854981{col 89}{space 3} 2.615669
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 1.973363{col 48}{space 2} .4197011{col 59}{space 1}    3.20{col 68}{space 3}0.001{col 76}{space 4} 1.300681{col 89}{space 3} 2.993941
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 3.143611{col 48}{space 2} .8209218{col 59}{space 1}    4.39{col 68}{space 3}0.000{col 76}{space 4} 1.884283{col 89}{space 3} 5.244588
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 1.631606{col 48}{space 2} .4996035{col 59}{space 1}    1.60{col 68}{space 3}0.110{col 76}{space 4} .8953118{col 89}{space 3}  2.97342
{txt}{space 31}53  {c |}{col 36}{res}{space 2} 1.469881{col 48}{space 2} .1580758{col 59}{space 1}    3.58{col 68}{space 3}0.000{col 76}{space 4} 1.190532{col 89}{space 3} 1.814776
{txt}{space 31}54  {c |}{col 36}{res}{space 2} 1.563619{col 48}{space 2}  .328335{col 59}{space 1}    2.13{col 68}{space 3}0.033{col 76}{space 4} 1.036077{col 89}{space 3}  2.35977
{txt}{space 31}55  {c |}{col 36}{res}{space 2} 1.059569{col 48}{space 2} .3512676{col 59}{space 1}    0.17{col 68}{space 3}0.861{col 76}{space 4} .5532732{col 89}{space 3} 2.029173
{txt}{space 31}56  {c |}{col 36}{res}{space 2} 1.014897{col 48}{space 2} .3769448{col 59}{space 1}    0.04{col 68}{space 3}0.968{col 76}{space 4} .4900898{col 89}{space 3} 2.101688
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 1.292011{col 48}{space 2} .4116784{col 59}{space 1}    0.80{col 68}{space 3}0.421{col 76}{space 4} .6919023{col 89}{space 3} 2.412614
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .3661048{col 48}{space 2} .0979398{col 59}{space 1}   -3.76{col 68}{space 3}0.000{col 76}{space 4} .2167172{col 89}{space 3} .6184684
{txt}{space 31}60  {c |}{col 36}{res}{space 2} 1.049365{col 48}{space 2} .1493331{col 59}{space 1}    0.34{col 68}{space 3}0.735{col 76}{space 4} .7939515{col 89}{space 3} 1.386946
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} .0614263{col 48}{space 2} .0554324{col 59}{space 1}   -3.09{col 68}{space 3}0.002{col 76}{space 4} .0104764{col 89}{space 3} .3601614
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2} .1580611{col 48}{space 2} .0922328{col 59}{space 1}   -3.16{col 68}{space 3}0.002{col 76}{space 4} .0503647{col 89}{space 3} .4960479
{txt}{space 27}clinton {c |}{col 36}{res}{space 2} .6640382{col 48}{space 2}  .351793{col 59}{space 1}   -0.77{col 68}{space 3}0.440{col 76}{space 4} .2350959{col 89}{space 3} 1.875604
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2}   .22793{col 48}{space 2} .1580071{col 59}{space 1}   -2.13{col 68}{space 3}0.033{col 76}{space 4} .0585768{col 89}{space 3} .8869048
{txt}{space 29}_cons {c |}{col 36}{res}{space 2} .0006716{col 48}{space 2} .0037413{col 59}{space 1}   -1.31{col 68}{space 3}0.190{col 76}{space 4} 1.22e-08{col 89}{space 3} 37.07202
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29}/ln_p {c |}{col 36}{res}{space 2} .9955563{col 48}{space 2} .0301441{col 59}{space 1}   33.03{col 68}{space 3}0.000{col 76}{space 4} .9364749{col 89}{space 3} 1.054638
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                 p {c |}{col 36}{res}{space 2} 2.706229{col 48}{space 2} .0815769{col 76}{space 4} 2.550973{col 89}{space 3} 2.870935
{txt}                               1/p {c |}{col 36}{res}{space 2} .3695178{col 48}{space 2} .0111388{col 76}{space 4} .3483186{col 89}{space 3} .3920073
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-830.8551{col 39}-492.2753{col 50}    31{col 58} 1046.551{col 69} 1194.015
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelE4
{txt}
{com}. estout modelE4, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                  modelE4   
{txt}                     b/se   
{txt}{hline 28}
{res}_t                          {txt}
{txt}zloyalmedian{res}        1.353*  {txt}
            {res}      (0.166)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.139   {txt}
            {res}      (0.208)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.630** {txt}
            {res}      (0.093)   {txt}
{txt}zpecompmed~n{res}        1.033   {txt}
            {res}      (0.081)   {txt}
{txt}zmecompmed~n{res}        0.990   {txt}
            {res}      (0.067)   {txt}
{txt}toplevel2   {res}        0.560***{txt}
            {res}      (0.066)   {txt}
{txt}presagency~n{res}        0.727   {txt}
            {res}      (0.200)   {txt}
{txt}presagency~d{res}        0.679   {txt}
            {res}      (0.188)   {txt}
{txt}subagencyd~n{res}        1.639*  {txt}
            {res}      (0.356)   {txt}
{txt}standalone~n{res}        1.793   {txt}
            {res}      (0.561)   {txt}
{txt}okstartsen~n{res}        0.000*  {txt}
            {res}      (0.000)   {txt}
{txt}okstartfil~e{res}      854.206** {txt}
            {res}   (1867.374)   {txt}
{txt}okcrossover {res}        0.167***{txt}
            {res}      (0.034)   {txt}
{txt}okstartpre~p{res}        0.990*  {txt}
            {res}      (0.005)   {txt}
{txt}okstartune~t{res}        1.132   {txt}
            {res}      (0.097)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.okstarta~r{res}        1.675*  {txt}
            {res}      (0.348)   {txt}
{txt}3.okstarta~r{res}        4.488***{txt}
            {res}      (0.897)   {txt}
{txt}4.okstarta~r{res}        4.262***{txt}
            {res}      (1.247)   {txt}
{txt}5.okstarta~r{res}        1.578   {txt}
            {res}      (0.390)   {txt}
{txt}6.okstarta~r{res}        3.643***{txt}
            {res}      (0.912)   {txt}
{txt}7.okstarta~r{res}        6.692***{txt}
            {res}      (1.998)   {txt}
{txt}8.okstarta~r{res}       10.898***{txt}
            {res}      (4.082)   {txt}
{txt}1.postempl~t{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.postempl~t{res}        0.711   {txt}
            {res}      (0.456)   {txt}
{txt}3.postempl~t{res}        0.756   {txt}
            {res}      (0.264)   {txt}
{txt}4.postempl~t{res}        0.734   {txt}
            {res}      (0.251)   {txt}
{txt}5.postempl~t{res}        0.519   {txt}
            {res}      (0.192)   {txt}
{txt}6.postempl~t{res}        0.571   {txt}
            {res}      (0.202)   {txt}
{txt}7.postempl~t{res}        0.620   {txt}
            {res}      (0.247)   {txt}
{txt}9999.poste~t{res}        0.616   {txt}
            {res}      (0.237)   {txt}
{txt}1.sbagency  {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.sbagency  {res}        2.826***{txt}
            {res}      (0.850)   {txt}
{txt}3.sbagency  {res}        1.870*  {txt}
            {res}      (0.531)   {txt}
{txt}4.sbagency  {res}        1.063   {txt}
            {res}      (0.255)   {txt}
{txt}5.sbagency  {res}        1.098   {txt}
            {res}      (0.296)   {txt}
{txt}6.sbagency  {res}        2.259***{txt}
            {res}      (0.526)   {txt}
{txt}7.sbagency  {res}        1.879   {txt}
            {res}      (0.608)   {txt}
{txt}8.sbagency  {res}        2.350** {txt}
            {res}      (0.688)   {txt}
{txt}9.sbagency  {res}        2.280** {txt}
            {res}      (0.680)   {txt}
{txt}11.sbagency {res}        3.654***{txt}
            {res}      (1.191)   {txt}
{txt}12.sbagency {res}        1.693*  {txt}
            {res}      (0.363)   {txt}
{txt}13.sbagency {res}        1.547   {txt}
            {res}      (0.400)   {txt}
{txt}14.sbagency {res}        2.444** {txt}
            {res}      (0.757)   {txt}
{txt}15.sbagency {res}        1.697   {txt}
            {res}      (0.469)   {txt}
{txt}16.sbagency {res}        0.857   {txt}
            {res}      (0.141)   {txt}
{txt}17.sbagency {res}        1.448*  {txt}
            {res}      (0.237)   {txt}
{txt}18.sbagency {res}        1.997*  {txt}
            {res}      (0.595)   {txt}
{txt}19.sbagency {res}        0.749   {txt}
            {res}      (0.120)   {txt}
{txt}20.sbagency {res}        0.290***{txt}
            {res}      (0.093)   {txt}
{txt}21.sbagency {res}        0.911   {txt}
            {res}      (0.070)   {txt}
{txt}22.sbagency {res}        0.483*  {txt}
            {res}      (0.167)   {txt}
{txt}23.sbagency {res}        1.025   {txt}
            {res}      (0.278)   {txt}
{txt}24.sbagency {res}        0.322** {txt}
            {res}      (0.141)   {txt}
{txt}25.sbagency {res}        1.503** {txt}
            {res}      (0.197)   {txt}
{txt}26.sbagency {res}        0.828   {txt}
            {res}      (0.132)   {txt}
{txt}27.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}28.sbagency {res}        1.490***{txt}
            {res}      (0.144)   {txt}
{txt}29.sbagency {res}        3.280** {txt}
            {res}      (1.190)   {txt}
{txt}30.sbagency {res}        1.339   {txt}
            {res}      (0.457)   {txt}
{txt}50.sbagency {res}        1.973** {txt}
            {res}      (0.420)   {txt}
{txt}51.sbagency {res}        3.144***{txt}
            {res}      (0.821)   {txt}
{txt}52.sbagency {res}        1.632   {txt}
            {res}      (0.500)   {txt}
{txt}53.sbagency {res}        1.470***{txt}
            {res}      (0.158)   {txt}
{txt}54.sbagency {res}        1.564*  {txt}
            {res}      (0.328)   {txt}
{txt}55.sbagency {res}        1.060   {txt}
            {res}      (0.351)   {txt}
{txt}56.sbagency {res}        1.015   {txt}
            {res}      (0.377)   {txt}
{txt}57.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}58.sbagency {res}        1.292   {txt}
            {res}      (0.412)   {txt}
{txt}59.sbagency {res}        0.366***{txt}
            {res}      (0.098)   {txt}
{txt}60.sbagency {res}        1.049   {txt}
            {res}      (0.149)   {txt}
{txt}61.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}reagan      {res}        0.061** {txt}
            {res}      (0.055)   {txt}
{txt}bush41      {res}        0.158** {txt}
            {res}      (0.092)   {txt}
{txt}clinton     {res}        0.664   {txt}
            {res}      (0.352)   {txt}
{txt}bush43      {res}        0.228*  {txt}
            {res}      (0.158)   {txt}
{txt}_cons       {res}        0.001   {txt}
            {res}      (0.004)   {txt}
{txt}{hline 28}
{res}/                           {txt}
{txt}ln_p        {res}        2.706***{txt}
            {res}      (0.082)   {txt}
{txt}{hline 28}

{com}. 
. 
. 
. *** COMPUTE Figure E1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [ME1−ME4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3653231 [0.9746053 - (-0.3853984)]
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3600037, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3600037

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5334881{col 26}{space 2} .1068252{col 37}{space 1}   -3.14{col 46}{space 3}0.002{col 54}{space 4} .3603138{col 67}{space 3} .7898935
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelE4zloyal = r(table)
{txt}
{com}. mat list modelE4zloyal
{res}
{txt}modelE4zloyal[9,1]
              (1)
     b {res} .53348806
{txt}    se {res} .10682516
{txt}     z {res} -3.137842
{txt}pvalue {res} .00170197
{txt}    ll {res}  .3603138
{txt}    ul {res} .78989346
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         1
{reset}
{com}. 
. 
. 
. 
. **** COMPUTE Figure E2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [ME1−ME4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. ** Re-Estimate Model E4  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.postemployment i.sbagency reagan bush41 clinton bush43, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-601.73178}  
Iteration 2:{space 3}log pseudolikelihood = {res:-493.59776}  
Iteration 3:{space 3}log pseudolikelihood = {res:-492.27816}  
Iteration 4:{space 3}log pseudolikelihood = {res:-492.27527}  
Iteration 5:{space 3}log pseudolikelihood = {res:-492.27527}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
{col 49}{help j_robustsingular##|_new:Wald chi2(29)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-492.27527             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.353125{col 40}{space 2} .1664157{col 51}{space 1}    2.46{col 60}{space 3}0.014{col 68}{space 4} 1.063291{col 81}{space 3} 1.721964
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.139224{col 40}{space 2} .2081116{col 51}{space 1}    0.71{col 60}{space 3}0.476{col 68}{space 4} .7963663{col 81}{space 3} 1.629691
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .6300238{col 40}{space 2} .0927611{col 51}{space 1}   -3.14{col 60}{space 3}0.002{col 68}{space 4} .4720967{col 81}{space 3} .8407811
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} 1.033273{col 40}{space 2} .0807359{col 51}{space 1}    0.42{col 60}{space 3}0.675{col 68}{space 4} .8865552{col 81}{space 3} 1.204273
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} .9899278{col 40}{space 2}  .066954{col 51}{space 1}   -0.15{col 60}{space 3}0.881{col 68}{space 4} .8670263{col 81}{space 3} 1.130251
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5601219{col 40}{space 2}  .065567{col 51}{space 1}   -4.95{col 60}{space 3}0.000{col 68}{space 4} .4452892{col 81}{space 3} .7045679
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} .7273595{col 40}{space 2} .2004698{col 51}{space 1}   -1.16{col 60}{space 3}0.248{col 68}{space 4} .4237861{col 81}{space 3} 1.248393
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} .6791953{col 40}{space 2}   .18798{col 51}{space 1}   -1.40{col 60}{space 3}0.162{col 68}{space 4} .3948287{col 81}{space 3} 1.168371
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.639018{col 40}{space 2} .3564941{col 51}{space 1}    2.27{col 60}{space 3}0.023{col 68}{space 4} 1.070147{col 81}{space 3} 2.510291
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} 1.793328{col 40}{space 2} .5608521{col 51}{space 1}    1.87{col 60}{space 3}0.062{col 68}{space 4} .9715202{col 81}{space 3} 3.310303
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 4.72e-11{col 40}{space 2} 4.91e-10{col 51}{space 1}   -2.29{col 60}{space 3}0.022{col 68}{space 4} 6.66e-20{col 81}{space 3} .0334842
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 854.2056{col 40}{space 2} 1867.374{col 51}{space 1}    3.09{col 60}{space 3}0.002{col 68}{space 4} 11.76943{col 81}{space 3} 61996.83
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .1674713{col 40}{space 2} .0344922{col 51}{space 1}   -8.68{col 60}{space 3}0.000{col 68}{space 4} .1118479{col 81}{space 3}  .250757
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2}  .990001{col 40}{space 2} .0046788{col 51}{space 1}   -2.13{col 60}{space 3}0.033{col 68}{space 4} .9808732{col 81}{space 3} .9992139
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} 1.131843{col 40}{space 2} .0974701{col 51}{space 1}    1.44{col 60}{space 3}0.150{col 68}{space 4} .9560569{col 81}{space 3}  1.33995
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2}  1.67478{col 40}{space 2}  .347953{col 51}{space 1}    2.48{col 60}{space 3}0.013{col 68}{space 4} 1.114581{col 81}{space 3} 2.516539
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 4.488338{col 40}{space 2}  .896839{col 51}{space 1}    7.51{col 60}{space 3}0.000{col 68}{space 4} 3.033908{col 81}{space 3}  6.64001
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 4.262204{col 40}{space 2} 1.247245{col 51}{space 1}    4.95{col 60}{space 3}0.000{col 68}{space 4}  2.40186{col 81}{space 3} 7.563462
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.578466{col 40}{space 2} .3897012{col 51}{space 1}    1.85{col 60}{space 3}0.064{col 68}{space 4} .9729384{col 81}{space 3} 2.560855
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 3.643104{col 40}{space 2} .9121521{col 51}{space 1}    5.16{col 60}{space 3}0.000{col 68}{space 4}  2.23023{col 81}{space 3} 5.951048
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 6.692187{col 40}{space 2}  1.99788{col 51}{space 1}    6.37{col 60}{space 3}0.000{col 68}{space 4} 3.727787{col 81}{space 3} 12.01393
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 10.89763{col 40}{space 2} 4.082211{col 51}{space 1}    6.38{col 60}{space 3}0.000{col 68}{space 4} 5.229678{col 81}{space 3} 22.70852
{txt}{space 26} {c |}
{space 12}postemployment {c |}
{space 24}2  {c |}{col 28}{res}{space 2} .7113708{col 40}{space 2} .4560284{col 51}{space 1}   -0.53{col 60}{space 3}0.595{col 68}{space 4} .2025015{col 81}{space 3} 2.498986
{txt}{space 24}3  {c |}{col 28}{res}{space 2} .7558988{col 40}{space 2} .2639055{col 51}{space 1}   -0.80{col 60}{space 3}0.423{col 68}{space 4} .3813162{col 81}{space 3} 1.498449
{txt}{space 24}4  {c |}{col 28}{res}{space 2} .7339528{col 40}{space 2} .2509247{col 51}{space 1}   -0.90{col 60}{space 3}0.366{col 68}{space 4} .3755418{col 81}{space 3} 1.434426
{txt}{space 24}5  {c |}{col 28}{res}{space 2} .5185161{col 40}{space 2} .1918947{col 51}{space 1}   -1.77{col 60}{space 3}0.076{col 68}{space 4} .2510417{col 81}{space 3} 1.070973
{txt}{space 24}6  {c |}{col 28}{res}{space 2} .5714293{col 40}{space 2} .2021806{col 51}{space 1}   -1.58{col 60}{space 3}0.114{col 68}{space 4} .2856236{col 81}{space 3} 1.143223
{txt}{space 24}7  {c |}{col 28}{res}{space 2} .6195403{col 40}{space 2} .2468016{col 51}{space 1}   -1.20{col 60}{space 3}0.229{col 68}{space 4} .2837807{col 81}{space 3} 1.352559
{txt}{space 21}9999  {c |}{col 28}{res}{space 2} .6163385{col 40}{space 2} .2372457{col 51}{space 1}   -1.26{col 60}{space 3}0.209{col 68}{space 4} .2898467{col 81}{space 3}   1.3106
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 2.825678{col 40}{space 2} .8495995{col 51}{space 1}    3.45{col 60}{space 3}0.001{col 68}{space 4} 1.567441{col 81}{space 3} 5.093946
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 1.870124{col 40}{space 2} .5305185{col 51}{space 1}    2.21{col 60}{space 3}0.027{col 68}{space 4} 1.072509{col 81}{space 3} 3.260919
{txt}{space 24}4  {c |}{col 28}{res}{space 2}  1.06328{col 40}{space 2} .2554927{col 51}{space 1}    0.26{col 60}{space 3}0.798{col 68}{space 4} .6639182{col 81}{space 3} 1.702866
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.097973{col 40}{space 2} .2959438{col 51}{space 1}    0.35{col 60}{space 3}0.729{col 68}{space 4} .6473837{col 81}{space 3} 1.862179
{txt}{space 24}6  {c |}{col 28}{res}{space 2}  2.25944{col 40}{space 2} .5262711{col 51}{space 1}    3.50{col 60}{space 3}0.000{col 68}{space 4} 1.431324{col 81}{space 3} 3.566675
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 1.879225{col 40}{space 2} .6077298{col 51}{space 1}    1.95{col 60}{space 3}0.051{col 68}{space 4} .9970234{col 81}{space 3} 3.542029
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 2.349701{col 40}{space 2} .6877356{col 51}{space 1}    2.92{col 60}{space 3}0.004{col 68}{space 4} 1.323957{col 81}{space 3} 4.170147
{txt}{space 24}9  {c |}{col 28}{res}{space 2} 2.280259{col 40}{space 2} .6802051{col 51}{space 1}    2.76{col 60}{space 3}0.006{col 68}{space 4} 1.270777{col 81}{space 3} 4.091655
{txt}{space 23}11  {c |}{col 28}{res}{space 2} 3.654478{col 40}{space 2} 1.191016{col 51}{space 1}    3.98{col 60}{space 3}0.000{col 68}{space 4} 1.929361{col 81}{space 3} 6.922086
{txt}{space 23}12  {c |}{col 28}{res}{space 2} 1.693453{col 40}{space 2} .3628639{col 51}{space 1}    2.46{col 60}{space 3}0.014{col 68}{space 4} 1.112711{col 81}{space 3} 2.577294
{txt}{space 23}13  {c |}{col 28}{res}{space 2}  1.54658{col 40}{space 2} .4004922{col 51}{space 1}    1.68{col 60}{space 3}0.092{col 68}{space 4} .9310025{col 81}{space 3} 2.569176
{txt}{space 23}14  {c |}{col 28}{res}{space 2} 2.443826{col 40}{space 2} .7572283{col 51}{space 1}    2.88{col 60}{space 3}0.004{col 68}{space 4} 1.331443{col 81}{space 3} 4.485576
{txt}{space 23}15  {c |}{col 28}{res}{space 2} 1.697156{col 40}{space 2} .4687935{col 51}{space 1}    1.91{col 60}{space 3}0.055{col 68}{space 4}  .987644{col 81}{space 3} 2.916374
{txt}{space 23}16  {c |}{col 28}{res}{space 2} .8566316{col 40}{space 2}   .14148{col 51}{space 1}   -0.94{col 60}{space 3}0.349{col 68}{space 4}  .619742{col 81}{space 3}  1.18407
{txt}{space 23}17  {c |}{col 28}{res}{space 2} 1.448179{col 40}{space 2} .2374233{col 51}{space 1}    2.26{col 60}{space 3}0.024{col 68}{space 4} 1.050198{col 81}{space 3} 1.996979
{txt}{space 23}18  {c |}{col 28}{res}{space 2}  1.99726{col 40}{space 2} .5946542{col 51}{space 1}    2.32{col 60}{space 3}0.020{col 68}{space 4}   1.1143{col 81}{space 3} 3.579869
{txt}{space 23}19  {c |}{col 28}{res}{space 2} .7490588{col 40}{space 2} .1199849{col 51}{space 1}   -1.80{col 60}{space 3}0.071{col 68}{space 4} .5472297{col 81}{space 3} 1.025326
{txt}{space 23}20  {c |}{col 28}{res}{space 2} .2897999{col 40}{space 2}  .093347{col 51}{space 1}   -3.85{col 60}{space 3}0.000{col 68}{space 4} .1541413{col 81}{space 3} .5448506
{txt}{space 23}21  {c |}{col 28}{res}{space 2} .9109079{col 40}{space 2}  .069936{col 51}{space 1}   -1.22{col 60}{space 3}0.224{col 68}{space 4} .7836506{col 81}{space 3} 1.058831
{txt}{space 23}22  {c |}{col 28}{res}{space 2} .4827715{col 40}{space 2} .1670143{col 51}{space 1}   -2.10{col 60}{space 3}0.035{col 68}{space 4} .2450583{col 81}{space 3}  .951073
{txt}{space 23}23  {c |}{col 28}{res}{space 2} 1.025106{col 40}{space 2} .2781774{col 51}{space 1}    0.09{col 60}{space 3}0.927{col 68}{space 4} .6022581{col 81}{space 3} 1.744836
{txt}{space 23}24  {c |}{col 28}{res}{space 2} .3216964{col 40}{space 2} .1409877{col 51}{space 1}   -2.59{col 60}{space 3}0.010{col 68}{space 4} .1362686{col 81}{space 3} .7594455
{txt}{space 23}25  {c |}{col 28}{res}{space 2} 1.502735{col 40}{space 2} .1972936{col 51}{space 1}    3.10{col 60}{space 3}0.002{col 68}{space 4} 1.161792{col 81}{space 3} 1.943732
{txt}{space 23}26  {c |}{col 28}{res}{space 2} .8284187{col 40}{space 2} .1315719{col 51}{space 1}   -1.19{col 60}{space 3}0.236{col 68}{space 4} .6068195{col 81}{space 3} 1.130942
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2} 1.490238{col 40}{space 2} .1440611{col 51}{space 1}    4.13{col 60}{space 3}0.000{col 68}{space 4} 1.233019{col 81}{space 3} 1.801114
{txt}{space 23}29  {c |}{col 28}{res}{space 2} 3.280295{col 40}{space 2} 1.189751{col 51}{space 1}    3.28{col 60}{space 3}0.001{col 68}{space 4} 1.611333{col 81}{space 3} 6.677912
{txt}{space 23}30  {c |}{col 28}{res}{space 2} 1.339043{col 40}{space 2} .4574451{col 51}{space 1}    0.85{col 60}{space 3}0.393{col 68}{space 4} .6854981{col 81}{space 3} 2.615669
{txt}{space 23}50  {c |}{col 28}{res}{space 2} 1.973363{col 40}{space 2} .4197011{col 51}{space 1}    3.20{col 60}{space 3}0.001{col 68}{space 4} 1.300681{col 81}{space 3} 2.993941
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 3.143611{col 40}{space 2} .8209218{col 51}{space 1}    4.39{col 60}{space 3}0.000{col 68}{space 4} 1.884283{col 81}{space 3} 5.244588
{txt}{space 23}52  {c |}{col 28}{res}{space 2} 1.631606{col 40}{space 2} .4996035{col 51}{space 1}    1.60{col 60}{space 3}0.110{col 68}{space 4} .8953118{col 81}{space 3}  2.97342
{txt}{space 23}53  {c |}{col 28}{res}{space 2} 1.469881{col 40}{space 2} .1580758{col 51}{space 1}    3.58{col 60}{space 3}0.000{col 68}{space 4} 1.190532{col 81}{space 3} 1.814776
{txt}{space 23}54  {c |}{col 28}{res}{space 2} 1.563619{col 40}{space 2}  .328335{col 51}{space 1}    2.13{col 60}{space 3}0.033{col 68}{space 4} 1.036077{col 81}{space 3}  2.35977
{txt}{space 23}55  {c |}{col 28}{res}{space 2} 1.059569{col 40}{space 2} .3512676{col 51}{space 1}    0.17{col 60}{space 3}0.861{col 68}{space 4} .5532732{col 81}{space 3} 2.029173
{txt}{space 23}56  {c |}{col 28}{res}{space 2} 1.014897{col 40}{space 2} .3769448{col 51}{space 1}    0.04{col 60}{space 3}0.968{col 68}{space 4} .4900898{col 81}{space 3} 2.101688
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2} 1.292011{col 40}{space 2} .4116784{col 51}{space 1}    0.80{col 60}{space 3}0.421{col 68}{space 4} .6919023{col 81}{space 3} 2.412614
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .3661048{col 40}{space 2} .0979398{col 51}{space 1}   -3.76{col 60}{space 3}0.000{col 68}{space 4} .2167172{col 81}{space 3} .6184684
{txt}{space 23}60  {c |}{col 28}{res}{space 2} 1.049365{col 40}{space 2} .1493331{col 51}{space 1}    0.34{col 60}{space 3}0.735{col 68}{space 4} .7939515{col 81}{space 3} 1.386946
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 26} {c |}
{space 20}reagan {c |}{col 28}{res}{space 2} .0614263{col 40}{space 2} .0554324{col 51}{space 1}   -3.09{col 60}{space 3}0.002{col 68}{space 4} .0104764{col 81}{space 3} .3601614
{txt}{space 20}bush41 {c |}{col 28}{res}{space 2} .1580611{col 40}{space 2} .0922328{col 51}{space 1}   -3.16{col 60}{space 3}0.002{col 68}{space 4} .0503647{col 81}{space 3} .4960479
{txt}{space 19}clinton {c |}{col 28}{res}{space 2} .6640382{col 40}{space 2}  .351793{col 51}{space 1}   -0.77{col 60}{space 3}0.440{col 68}{space 4} .2350959{col 81}{space 3} 1.875604
{txt}{space 20}bush43 {c |}{col 28}{res}{space 2}   .22793{col 40}{space 2} .1580071{col 51}{space 1}   -2.13{col 60}{space 3}0.033{col 68}{space 4} .0585768{col 81}{space 3} .8869048
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0006716{col 40}{space 2} .0037413{col 51}{space 1}   -1.31{col 60}{space 3}0.190{col 68}{space 4} 1.22e-08{col 81}{space 3} 37.07202
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} .9955563{col 40}{space 2} .0301441{col 51}{space 1}   33.03{col 60}{space 3}0.000{col 68}{space 4} .9364749{col 81}{space 3} 1.054638
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.706229{col 40}{space 2} .0815769{col 68}{space 4} 2.550973{col 81}{space 3} 2.870935
{txt}                       1/p {c |}{col 28}{res}{space 2} .3695178{col 40}{space 2} .0111388{col 68}{space 4} .3483186{col 81}{space 3} .3920073
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimates store modelE4a
{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 925.4483{col 26}{space 2} 27.33531{col 37}{space 1}   33.86{col 46}{space 3}0.000{col 54}{space 4} 871.8721{col 67}{space 3} 979.0245
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1168.366{col 26}{space 2} 61.73865{col 37}{space 1}   18.92{col 46}{space 3}0.000{col 54}{space 4}  1047.36{col 67}{space 3} 1289.371
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     8.86{col 38}{space 2}   0.0029
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 242.9175{col 26}{space 2} 81.60549{col 37}{space 5} 82.97368{col 51}{space 3} 402.8613
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelE4azloyal = r(table)
{txt}
{com}. mat list modelE4azloyal
{res}
{txt}modelE4azloyal[9,1]
            r2vs1.
              _at
     b {res}  242.9175
{txt}    se {res} 81.605489
{txt}     z {res} 2.9767299
{txt}pvalue {res} .00291341
{txt}    ll {res} 82.973677
{txt}    ul {res} 402.86131
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. estimates restore modelE4a
{txt}(results {stata estimates replay modelE4a:modelE4a} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 886.9142{col 26}{space 2} 36.91122{col 37}{space 1}   24.03{col 46}{space 3}0.000{col 54}{space 4} 814.5695{col 67}{space 3} 959.2588
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1326.161{col 26}{space 2} 122.5519{col 37}{space 1}   10.82{col 46}{space 3}0.000{col 54}{space 4} 1085.964{col 67}{space 3} 1566.358
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     8.06{col 38}{space 2}   0.0045
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 439.2467{col 26}{space 2} 154.7495{col 37}{space 5} 135.9433{col 51}{space 3} 742.5502
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelE4bzloyal = r(table)
{txt}
{com}. mat list modelE4bzloyal
{res}
{txt}modelE4bzloyal[9,1]
            r2vs1.
              _at
     b {res} 439.24672
{txt}    se {res} 154.74951
{txt}     z {res} 2.8384369
{txt}pvalue {res} .00453351
{txt}    ll {res} 135.94325
{txt}    ul {res} 742.55018
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. *Figure E1
. 
. matrix pointmodel = modelE1zloyal[1,1], modelE2zloyal[1,1], modelE3zloyal[1,1], modelE4zloyal[1,1]
{txt}
{com}. 
. *
. matrix cimodel = (modelE1zloyal[5,1], modelE2zloyal[5,1], modelE3zloyal[5,1], modelE4zloyal[5,1] \ modelE1zloyal[6,1], modelE2zloyal[6,1], modelE3zloyal[6,1], modelE4zloyal[6,1])
{txt}
{com}. 
. coefplot (matrix(pointmodel), ci((cimodel))), grid(none) xline(1, lcolor(red%40) lpattern(dash)) xtitle("Hazard Ratio", size(small) margin(t=2)) ylabel(1 "Model E1"  2 "Model E2"  3 "Model E3" 4 "Model E4", labsize(small) noticks) mlabel format(%9.3f) mlabposition(12) mlabsize(vsmall) xlabel(0(1)2, angle(0) labsize(small) format(%9.1f)) msymbol(o) mcolor(black) msize(small) title("FIGURE E1", size(small)) ciopts(lcolor(black)) legend(off) subtitle("Marginal Differential Effect of Presidential Loyalty on Appointee Tenure Hazard" "[Policy Priority Agencies versus Non-Policy Priority Agencies]", size(small))
{res}{txt}
{com}. 
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureE1.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureE1.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureE1.gph saved)

{com}. 
. 
. 
. 
. 
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. *Figure E2
. 
. matrix pointmodelE1 = modelE3azloyal[1,1], modelE3bzloyal[1,1], modelE4azloyal[1,1], modelE4bzloyal[1,1]
{txt}
{com}. 
. *
. matrix cimodel1 = (modelE3azloyal[5,1], modelE3bzloyal[5,1], modelE4azloyal[5,1], modelE4bzloyal[5,1] \ modelE3azloyal[6,1], modelE3bzloyal[6,1], modelE4azloyal[6,1], modelE4bzloyal[6,1])
{txt}
{com}. 
. coefplot (matrix(pointmodelE1), ci((cimodel1))), grid(none) xtitle("Predicted Number of Days", size(small) margin(t=2)) ylabel(1 `" "Model E3" "Interquartile Change" "' 2 `" "Model E3" "Interdecile Change" "' 3 `" "Model E4" "Interquartile Change" "'4 `" "Model E4" "Interdecile Change" "', labsize(small) noticks) mlabel format(%9.0f) mlabposition(12) mlabsize(vsmall) xlabel(0(100)800, angle(0) labsize(small) format(%9.0f))   msymbol(o) mcolor(black) msize(small) title("FIGURE E2", size(small)) ciopts(lcolor(black)) legend(off) subtitle("Marginal Differential Effect of Presidential Loyalty on Median Appointee Tenure" "[Policy Priority Agencies versus Non-Policy Priority Agencies]", size(small))
{res}{txt}
{com}. 
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureE2.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureE2.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureE2.gph saved)

{com}. 
. 
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.APPENDIX E.04-21-2023.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}22 Apr 2023, 09:54:48
{txt}{.-}
{smcl}
{txt}{sf}{ul off}